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Using ChatGPT in Undergraduate computer science and Software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer science and Software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile Software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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Efficient Compressed Sensing for Real-Time Electrocardiogram Acquisition on Low-Power Medical Devices

Efficient Compressed Sensing for Real-Time Electrocardiogram...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Terrell, Mitchell Weissman, Jon University of Minnesota Dept. of Computer Science and Engineering MinneapolisMN United States

ISBN: (纸本)9798350351552

Low cost wearable and implantable cardiac monitoring devices (WCM & ICM), combined with increasingly accurate disease and arrhythmia detection algorithms have proven effective to help slow the impact of cardiovascular disease, the worlds leading cause of death in 2022 [1]. Improvements to server-side detection algorithms along with hardware limitations of these devices such as slow processor speeds and minimal battery life has led to a desire to offload data from the devices for later analysis. Paired with limited storage capacity, this has led to a push to decrease the necessary storage for electrocardiogram (EKG) signals without sacrificing disease detection accuracy and device longevity. A promising recent innovation in EKG compression has come from Compressed Sensing (CS), which exposes inherent sparseness in the signal to selectively sample for eventual server-side reconstruction. Many CS approaches have been implemented on WCM devices which demonstrate a high compression ratio (CR) and accurate signal reconstruction, but quickly become impractical for the stricter hardware constraints of ICM devices due to the increased computation on the device and slow reconstruction time. In this paper we propose a CS approach known as Tailored Sensing (TS) which combines on-device prior knowledge, a custom transform basis, and optimal sense location selection to achieve improved CR and reconstruction accuracy while eliminating on-device computational burden and slow reconstruction time. Our approach offers equivalent or better signal reconstruction at previously unfathomable CRs due to our novel signal segmentation scheme. Additionally, our approach boasts a zero overhead on-device sensing strategy and a 93 % reduction in signal reconstruction time. © 2024 IEEE.

关键词： Digital storage

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Smart Healthcare - IoT and Artificial Neural Network(ANN) 15

Smart Healthcare - IoT and Artificial Neural Network(ANN)

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sutar, Laxmikanta Chinara, Suchismita National Institute of Technology Rourkela Dept. of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350370249

With the use of the Internet of Medical Things (IoMT), the growing accessibility of wearable devices, and the continual reduction in the cost of sensor-based technologies and green computing, internet-connected sensors allow users to track, analyze, and share welfare information employing a cloud-based platform. Artificial Neural Networks (ANN) find extensive use across various applications, including healthcare systems, drawing inspiration from the biological concept of neurons. This study explores the prediction of heart attack risks using a combination of Internet of Things (IoT) sensors and artificial neural networks (ANN). Through the integration of IoT sensors such as the pulse sensor and LM35 temperature sensor, essential physiological data related to heart rate and body temperature was collected. An ANN model was developed, focusing on predictive capabilities using the sigmoid function, feedforward and backpropagation techniques, and random weight initialization. The investigation further utilized Keras with TensorFlow and optimized model parameters using Adam Optimization and Later with One Hot Encoding plus a Genetic Algorithm (GA). Additionally, the study involved training neural networks on higher-quality UCI datasets to evaluate performance. Scatterplots were generated to visualize relationships between variables. Results demonstrated promising performance improvements, with the model achieving close to 90% accuracy on the testing dataset. Encoding techniques were employed to handle categorical variables effectively, contributing to the model's robustness and generalization. Overall, this research offers valuable insights into leveraging IoT sensors and ANN for heart attack risk prediction. © 2024 IEEE.

关键词： Adam Optimisation ANN Back Propagation Dense Layers Feed Forwarding Fuzzy Genetic Algorithm HTTP IoT MQTT One Hot Encoding Protocols UCI Heart Data

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A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels 2

A Novel Approach for Single-Shot Target Recognition by Match...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. of Computer Science & Engineering National Institute of Technology Uttarakhand 246174 India

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition;Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Dirt Identification and Removal based on Non-Contact Infrared Temperature Sensor on Solar Panel 7

Dirt Identification and Removal based on Non-Contact Infrare...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rupin, Rezmah Jemima Hasan, Imran Jahan, Rownak Haque Ontor, Md. Rakibul Islam, Md. Motaharul East West University Dept. of Computer Science Engineering Dhaka Bangladesh United International University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341485

Temperature sensing is a technique to detect dirt on a solar-panel-surface that can influence its surface temperature and can reduce its performance, which necessitates monitoring to optimize solar panel output generation, reduce unnecessary costs, and perform efficient cleaning. Temperature sensing can achieve faster and more accurate detection of dirt without using any robotic cameras or manual observation. In this study, a method is proposed to detect dirt on a solar panel with the help of an MLX90614 sensor, a DHT22 sensor connecting with Arduino UNO components. DC motors and gear motors are being used for the removal of accumulated dirt. The temperature of the solar panel surface is measured by an MLX90614 sensor using infrared radiation, while the environment's humidity and temperature are measured by a DHT22 sensor. The system implies measuring the solar panel's temperature and comparing it to the ambient temperature. This system aims at improving previously proposed dirt detection methods using this approach and suggests a motor-driven dirt removal technique. © 2023 IEEE.

关键词： Solar panels

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Detection of Specific Language Impairment Using XGBoost Model 15

Detection of Specific Language Impairment Using XGBoost Mode...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rai, Rajnish Thenkanidiyoor, Veena Dept. of Computer Science and Engineering National Institute of Technology Goa Goa India

ISBN: (纸本)9798350370249

Specific Language Impairment (SLI) in children, a neurodevelopmental disorder presenting significant hurdles in language acquisition. Motivated by the prevalence and complexity of SLI diagnosis, this article introduces an methodology using Mel-Frequency Cepstral Coefficients (MFCC) and chroma features combined with the XGBoost algorithm for classification. By analyzing a diverse dataset encompassing both SLI-diagnosed and non SLI children, the proposed model achieves efficiency of 99.3% accuracy on unseen data. Notably, the model’s strategic feature selection and utilization of XGBoost demonstrate superior performance compared to existing methods, highlighting its importance in SLI diagnosis and intervention planning. This work significantly advances machine learning-based diagnostic tools and holds promise for improving the quality of life for individuals impacted by SLI. ©2024 IEEE.

关键词： Adversarial machine learning

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Novel Approach to Monitor Location of the Student in the Institution 15

Novel Approach to Monitor Location of the Student in the Ins...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kulkarni, R.N. Suma, Rochana Y. Sravanthi, B. Rakshitha, G. Krathika, T. Ballari Institute of Technology and Management Dept. of Computer Science & Engineering Ballari India

ISBN: (纸本)9798331300579

During the last few decades there is a tremendous growth in the information and communication technology wherein many organizations are making use of the hardware and software for various applications. Marking the attendance of a student in the school or college as well as the presence of the employee in the workplace is to be either captured or manually marked in the registers. Performing various analyses by taking the manual input is cumbersome because the number of students in the class or number of employees in the organizations are increasing. To overcome the difficulties of managing the things manually in this paper we are proposing an automated tool to capture the presence of the student in the specific location. This paper proposes a novel approach to mark the presence of a student in the college premises. The tool developed here is an Android Application (App) wherein the students can send the details to the App. Two modules are proposed here, one for the student and another for the Administrator. Through the student module, each student can login to the App and capture the self-image and send it to the Administrator Module. The captured image comprises the GPS details like Longitude, Latitude, Time, and date information. The information sent through the App determines the location of the student whether the student is in the college premise such as Class room, Laboratory and Library. The application which is managed by the teacher or the administrator or proctor will get the details about the student's image, GPS locations such as latitude, longitude, date and time. The mobile app verifies the GPS location and image of the student and compares it with the database. If the captured information matches with the appropriate GPS location then the students attendance is marked as present in that particular duration and appropriate files are updated. © Grenze Scientific Society, 2024.

关键词： Face recognition

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An Efficient Approach for Maintaining and Mining Frequent Episodes 6

An Efficient Approach for Maintaining and Mining Frequent Ep...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yen, Show-Jane Lee, Yue-Shi Ming Chuan University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350344349

Data mining technology plays a crucial role in the area of data analysis. Mining frequent episodes stands out as a pivotal task within this domain, enabling users to forecast future events based on present occurrences. Conventional methods for discovering frequent episodes typically follow a hierarchical approach, involving the generation of candidate episodes and subsequent scanning of sequence data to count their frequency, which can be quite time-consuming, as it necessitates repeated scans of the sequence data and the search for candidate episodes. In this paper, we introduce a novel approach for episode mining in a data stream. Our method distinguishes itself by scanning newly added data to update existing frequent episodes, all without the need for scanning the original data or searching for candidate episodes. The experiments also show that our approach is more efficient compared to other existing methods. © 2024 IEEE.

关键词： Data mining

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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural Network Technique for Event Detection in Video Sequences 1

An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural N...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Sachdeva, Kumud Sandhu, Jasminder Kaur Sahu, Rakesh Chandigarh University Dept. of Computer Science Engineering Punjab Gharuan140413 India

ISBN: (纸本)9798350361780

Classifying and detecting difficult video events based on visual modalities remains an uncertain problem. Conventional video presentation approaches ineffectively extract each modality, hindering video event detection (VED) rates. The research methodology involves pre-processing steps: uploading video data samples, extracting video frames, converting to grayscale (rgb2gray), enhancing, and calculating smooth frames from the UCF -101 dataset. This pre-processing phase aims to deliver high-quality frames without data loss. Next, the feature extraction process employs the HoG method to extract feature vectors from refined video frames, facilitating the training process and efficiently reducing dimensions. An adaptive, nature-inspired PBC method is then implemented to select reliable and optimized feature sets from the extracted ones. These selected optimized feature vectors are input into different events of the deep neural network (DNN) classifier. Finally, reliable feature sets are identified through feature matching, and experimental outcomes demonstrate a significant 21.3% enhancement in VED and classification, assessed through accuracy rate, specificity (SP), sensitivity (SN), etc. Compared with traditional approaches using manually designed feature sets, the proposed approach proves more effective. Simulation outcomes on publicly available VED databases consistently outperform state-of-the-art video representation methods such as EFS-linear multi-support vector machine (MSVM), convolutional neural network (CNN), etc. © 2024 IEEE.

关键词： Deep neural networks

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