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Automated Exam Script Checking using Zero-Shot LLM and Adaptive Generative AI

Automated Exam Script Checking using Zero-Shot LLM and Adapt...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Himi, Shinthi Tasnim Tanzila Monalisa, Natasha Sultana, Shirin Afrin, Anika Hasib, Khan Md Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The manual grading of exam scripts is a labor-intensive process, often plagued by subjectivity and inconsistency. This study presents an innovative approach to automating the evaluation of exam scripts using the Large Language Model (LLM) integrated with Zero-Shot Learning (ZSL) and Generative AI. Our system leverages the capabilities of GPT-4 to generate and evaluate answers, applying similarity measures to ensure accuracy and fairness in grading. The model's adaptability through ZSL allows it to assess new questions without additional training. The system also incorporates iterative refinement techniques to enhance the quality of standard answers over time. Performance evaluations demonstrate a low error rate, with an average relative error of 1.29% for annotated questions and 1.67% for non-annotated questions compared to human graders. The performance underscores the automated system's transformative potential in revolutionizing educational assessments, offering a consistent, fair, and highly efficient alternative to traditional grading methods. © 2024 IEEE.

关键词： Zero-shot learning

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5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

5G User Equipment (UE) Positioning and Localization Estimati...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

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Deep Learning Based Intrusion Detection for Cybersecurity in Unmanned Aerial Vehicles Network 4

Deep Learning Based Intrusion Detection for Cybersecurity in...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Niyonsaba, Simon Konate, Karim Soidridine, Moussa Moindze Dept. Mathematics and Computer Science Dakar Senegal

ISBN: (纸本)9798350395914

In this article, we have proposed Deep Learning techniques for cybersecurity in Unmanned Aerial Vehicles (UAVs). UAVs, also known as drones, have become versatile tools used in many applications. However, UA V cybersecurity remains a major concern due to the evolution of cyberattacks. Faced with this issue, it is important to give a contribution on intrusion detection systems (IDS) to be applied in UAVs, given that existing cybersecurity solutions still present limitations. It is in this context that we have proposed Deep Learning-based intrusion detection models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models (CNN+LSTM) and these are evaluated using the CICIDS2017 dataset. Based on their performance, the proposed models are compared with Machine Learning models to decide on the best model for intrusion detection in UAVs. Thus, the results obtained during the experiment indicate that the hybrid CNN-LSTM model obtained a high accuracy of 99.063% compared to the other models. © 2024 IEEE.

关键词： Network intrusion

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Adchat-TVQA: Innovative Application of LLMs-Based Text-Visual Question Answering Method in Advertising Legal Compliance Review 5

Adchat-TVQA: Innovative Application of LLMs-Based Text-Visua...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Gao, Ruiling Hou, Yingqi Tang, Weijie Zhang, Jin Hu, Yanbo Huang, Haiyun Tan, Wen'an Li, Liping Shanghai Polytechnic University Dept. of Computer Engineering Shanghai China Donghua University Dept. of Computer Science Shanghai China Tufts University Dept. of Computer Science MedfordMA United States

ISBN: (纸本)9798331530334

Advertising legal compliance reviews have always been time-consuming and labor-intensive, and existing Large Language Models(LLMs) are far worse performing than senior industry experts. In this paper, we propose a novel LLMs-based text-visual question answering method called Adchat-TVQA for advertising legal compliance review. After reading an advertising image and the corresponding review question, it will first understand the content on the image through multimodal learning, then optimize the question with Zero-Shot Chain-of-Thought (CoT) prompting method based on predefined industry expert experience, and finally input the augmented question into the large language model for inquiry. The feasibility of this method is verified through system prototype implementation and end-to-end functional tests. In addition, we summarized the characteristics of advertising images and added image segmentation and text box transpose to the data processing process for system performance improvement. Performance testing of the text visual cognitive question task on the AIWIN2021 dataset shows that the method scores higher than Microsoft's LayoutLM, with an increasement of the score from 51.52% to 52.74%. © 2024 IEEE.

关键词： Marketing

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Inspiring Entrepreneurship by Spreading New Technologies and Innovative Methods that Popularise science and Art 19

Inspiring Entrepreneurship by Spreading New Technologies and...

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19th European Conference on Innovation and Entrepreneurship, ECIE 2024

作者： Ophir, Dan Dept. of Computer Science Ariel University Israel

ISBN: (纸本)9781917204149

In recent years a new term related to GPT (Generative Pre-trained Transformer) has become very popular among professional society members who develop and use computing applications. This trend has inspired entrepreneurship in education in the following aspects: Academic - in the computer science and Management Departments Non-academic - studies for high school pupils and for adults who wish to enrich their education. The purpose of these studies is to popularise science, technology, and art using innovative methods to make these subjects attractive. Academic entrepreneurship requires students to be innovative, namely, to create a project using their acquired professional skills. The work, called the final project, is a part of the students' academic requirements at universities. As a promoter of these projects, I have been giving the students proposals, suggestions, and inspiration for the subjects of their projects. The article mentions and describes a database with hundreds of recommendations for subjects for projects as well as projects completed by students who previously finished their final project. This project's database is used as a model for the current students. The database includes the project's hierarchical classification along with subsections, according to their theme, innovation level, and completeness, indicating their practical or theoretical properties. The following projects are a sample demonstrating the variety of the final projects, for example: "Analysing the voiceless languages", "Double buffer economic boiler", and "Creating the most consensual coalition". The second aspect of the entrepreneurship being developed is a series of lectures "The Popularisation of the science and the Art". This collection of lectures includes an innovative method for preparing and presenting the lectures, connecting attractively the way in which science and art are popularised together for an audience in a wide spectrum of society. The Power-Point presentations

关键词： Students

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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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Integrated IoT and Machine Learning Solutions for Precision Farming: Crop Recommendation and Leaf Disease Diagnosis 8

Integrated IoT and Machine Learning Solutions for Precision ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Harish, M. Devi, J. Vimala Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376425

The digital convergence of the Internet of Things and machine learning has therefore redefined precision agriculture with respect to productivity and sustainability. This work therefore proposes a smart agriculture system via the following IoT sensors: Soil Moisture, DHT, and NPK sensors, along with data storage in ThingSpeak cloud *** Similarly, farmers learn which plants are best to grow based on their projected data from machine learning algorithms. A convolutional neural network named ResNet was used for its control to instantly and correctly identify plant diseases in live mode. For the system's front end, HTML, CSS, and JavaScript were used while Python facilitated creation of the back end that provides users with an easy-to-use interface. It has achieved high accuracy levels in crop recommendation through models like Decision Tree, Naive Bayes, SVM, Logistic Regression, Random Forest and XGBoost among others have attained an accuracy of 90%, 99.09%, 97.95%, 95.27%, 99.09% and 99.31% respectively in crop recommendation. By ResNet model in the identification of leaf disease, a density of 99.2% was achieved. © 2024 IEEE.

关键词： Smart agriculture

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Performance Evaluation of Multi-Disease Classification and Recommendation Models 3

Performance Evaluation of Multi-Disease Classification and R...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Singh, Lakhvinder Dalip Dept. of Computer Science & Applications Ambala India

ISBN: (纸本)9798331518592

This paper aims to review the current advances in healthcare uses of machine learning (ML) for the diagnosis of numerous diseases using different classification models. It proposes an efficient mechanism for multi-disease diagnosis by implementing the Bee-Featured Convolutional Neural Network (BFCNN), coupled with a Hybrid Statistical Fuzzy (Hybrid S-Fuzzy) scheme, to improve classification efficiency. The primary goal is to develop an accurate diagnostic system for heart, kidney, and liver diseases at early stages. The methodology presented compares several common ML models-Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Fast Condition Mutual Information (FCMIM-SVM)-using datasets from various sources with the BFCNN model. According to the simulated results, the proposed BFCNN model achieved a 96.7% accuracy rate and outperformed existing models in terms of sensitivity and specificity. This study suggests that using the BFCNN together with the Hybrid S-Fuzzy model enhances diagnosis for multiple diseases, offering higher accuracy and earlier identification compared to individual models, with up to 78% potential benefits in healthcare. Further studies on this model may lead to the application of deeper methods to increase diagnostic accuracy and improve treatment recommendations. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Agritech : An Enhanced Solution for Advanced Plant Disease Detection, Remediation, and Data-Driven Agriculture 2

Smart Agritech : An Enhanced Solution for Advanced Plant Dis...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Rekha, K.S. Haridas, Aakash Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Plant diseases can have a detrimental effect on crop yield and quality, posing significant challenges to global food security. Early and accurate disease detection is essential for timely intervention and mitigation. In this study, we propose a novel approach for plant disease detection using a deep learning method based on a state-of-the-art convolutional neural network, ResNet9 architecture. The ResNet9 model is trained on a diverse dataset of plant images, including both healthy and diseased plants, representing different plant species and diseases. Our method leverages the power of deep learning to automatically learn discriminative features from plant images, enabling it to distinguish between healthy and diseased plants with a high degree of accuracy. We evaluate the performance of our model on a real-world dataset of plant images, and the results demonstrate its effectiveness in disease detection. The model achieves a high accuracy rate, indicating its potential for field deployment to assist farmers in monitoring and managing plant health. In conclusion, our study presents a promising solution for automated plant disease detection, providing farmers and agronomists with a practical tool to identify and combat plant diseases early, ultimately contributing to improving crop productivity and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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