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A Systematic Literature Review on Recent Peer Code Review Implementation in Education

A Systematic Literature Review on Recent Peer Code Review Im...

引用

2024 international conference on TVET Excellence and Development, ICTeD 2024

作者： Ismail, Muhammad Huzaifah Ismail, Shahrinaz Noraa, Muhammad Nur Affendy Sijore, Jeffery Jeselee Hamid, Ainul Hazmin A Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Asia Pacific University of Technology and Innovation School of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798331516413

Regardless of the years of implementation, peer assessment is still with some limitations and could be applied to technical courses like computer science and software engineering, as much as theoretical courses are covered by most research. To add salt to this, the trend among the digital natives of the current generation of tertiary education students has brought up the need to explore gamification in learning and teaching assessments. This paper presents a systematic literature review on peer code review implementation in education, which highlights recent articles found from 2019 to 2024 on the related domain. Significant findings emerged from the search on six databases of scholarly articles as three categories of peer code review studies are found, namely empirical, approach-focused, and system-based implementation. These categories are further defined to differentiate the studies in terms of methods. The expected outcome of this study is to open a new path towards research on peer code review methods, which would include the elements appreciated by the new generation, such as gamification. © 2024 IEEE.

关键词： Gamification

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Breath Sound Acquisition in Different Environments 11

Breath Sound Acquisition in Different Environments

引用

11th international conference on Electrical, Electronic and computing engineering, IcETRAN 2024

作者： Ciric, Dejan Bogunovic, Durad Jankovic, Marko Miletic, Miljan University of Niš Faculty of Electronic Engineering in Niš Niš Serbia Academy of Applied Preschool Teaching and Health Studies Serbia

ISBN: (纸本)9798350386998

Breathing is one of the vital functions of a human. This is why the monitoring of breathing represents an important tool for diagnosing a medical condition in a patient, that is, for assessing health status. Apart from medical diagnostics, breath monitoring can be used for other purposes such as managing stress, optimizing sports performance, security applications, or human-computer interaction. Breath detection employs diverse sensors and approaches. The initial step in this process involves acquiring breath sound. This paper explores the potential for acquiring breath sound using several types of microphones in different environments. To this end, four microphones of varying types (digital MEMS, electret, studio condenser, and smartphone microphone) are utilized. Acquisition is conducted in two distinct environments or scenarios: with the microphone in the air in close proximity to the nose and mouth, and with the microphone in a military gas mask. The recorded signals are subsequently analyzed in both the time and spectral domains. © 2024 IEEE.

关键词： Microphones

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Student performance prediction in e-learning system and evaluating effectiveness of online courses

Student performance prediction in e-learning system and eval...

引用

2023 international conference on Advances in Intelligent computing and Applications, AICAPS 2023

作者： Blessy Paul, P. Kurian, Cini Mg University School of Computer Sciences Kerala Kottayam India Al-Ameen College Kerala Edathala India

ISBN: (纸本)9798350333817

In recent years, the number of online courses in India has skyrocketed especially due to the Covid pandemic. The most significant increments have happened in degree colleges, where 85% concur that internet based courses are important for their drawn-out procedure when contrasted with 60% in 2015. The distribution of online courses has evolved dramatically as technology has advanced. Web-based platform provides new challenges for both teachers and students. Teachers should be clear about the effectiveness of online learning in teaching students. For that, the possibilities of online learning should be compared with traditional learning. Students are evaluated based on their focus on online learning. This study aims to determine the efficacy of online courses by predicting student performance in an e-learning system. These research findings evaluate modern learning methods, highlight students' potential and help teachers understand how to assess and lead students on online platforms. © 2023 IEEE.

关键词： Decision trees

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Sentiment Recognition of Hinglish Code Mixed Data using Deep learning Models based Approach 13

Sentiment Recognition of Hinglish Code Mixed Data using Deep...

引用

13th international conference on Cloud computing, Data Science and engineering, Confluence 2023

作者： Das, Shubham Singh, Tanya Amity University Aset Noida India

ISBN: (纸本)9781665462631

Sentiment analysis in social media content gained popularity over the past several years due to its various uses in understanding human computer interaction, understanding consumers behavior, psychology, smart system, etc. Due to the massive volumes of data available via social media, which is commonly used for expressing opinions and ideas, this issue has drawn a lot of attention. To identify emotions, this research will make use of a labelled Hinglish dataset. Deep learning-based techniques are utilized to identify emotions in tweets with mixed Hindi-English coding by utilizing transformer-based models and multilingual word embeddings derived from FastText methods. Various deep learning models, such as convolutional neural networks (CNN), long short term memory (LSTM), and bi-directional long short term memory (Bi- LSTM) have been used to analyze sentiments. In comparison to other models, the convolutional neural network (CNN) achieved the highest accuracy, 75.25%. © 2023 IEEE.

关键词： Long short-term memory

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A Multilevel learning Model for Predicting CPU Utilization in Cloud Data Centers

A Multilevel Learning Model for Predicting CPU Utilization i...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Concordia University Electrical and Computer Engineering Montreal Canada Jordan University of Science and Technology Computer Science Irbid Jordan

ISBN: (纸本)9798350304602

In the contemporary era of cloud computing, efficient and precise prediction of CPU utilization ensures optimal performance and energy efficiency in data centers. Traditional predictive models often need to be improved as these data centers grow in complexity and scale, necessitating more nuanced and integrative solutions. This paper introduces an advanced multi-layered learning framework meticulously designed to meet the demands of modern cloud data centers. Our innovative approach synergistically combines anomaly detection, data clustering, and ensemble-based regression prediction. The Isolation Forest algorithm is used during the preliminary stage to identify and address anomalies within the data. Subsequent phases harness the K-Means clustering algorithm, refine data categorization based on recurrent CPU usage patterns, and employ multilevel ensemble-based prediction models for accurate forecasting rooted in historical and real-time data trends. Through comprehensive evaluations, our model demonstrates significant improvements in prediction accuracy and robustness against the dynamism inherent in cloud environments. Our research paves the way for a more resilient, proactive, and efficient approach to CPU utilization prediction, laying the foundational stone for future innovations in cloud computing resource management. © 2023 IEEE.

关键词： Forecasting

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CropSmart-A Crop Prediction Application Using Machine learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

引用

12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Comparative Analysis of Machine learning Methods for Plant Disease Identification 13

Comparative Analysis of Machine Learning Methods for Plant D...

引用

13th international conference on Cloud computing, Data Science and engineering, Confluence 2023

作者： Dubey, Alok Batra, Devansh Kumar, Gaurav Kumar, Sumit Singh, Mayank Jss Academy of Technical Education Uttar Pradesh Noida India Amity University Uttar Pradesh Noida India

ISBN: (纸本)9781665462631

Recently, agriculture domain has shown a digital shift that has automated various managerial task. The development of precision-based farming systems has overcome many challenges. Agriculture and collaborative activities contributed to almost 59% of the total public compensation. Plant diseases can cause major losses in agricultural product output, economy, quality, and quantity under certain conditions. It is possible to predict plant illnesses at early stage and take the necessary safeguards to avoid losses by utilizing machine learning methods. In this study, variety of machine learning techniques are used to identify plant diseases. The comparative analysis are performed using Naive Bayes, K- Nearest Neighbor, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) algorithms. According to performance, the quality measures are computed by determining accuracy, precision, recall, and fl-score. © 2023 IEEE.

关键词： Feature extraction

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Implementing Resource-Aware Scheduling Algorithm for Improving Cost Optimization in Cloud computing 4

Implementing Resource-Aware Scheduling Algorithm for Improvi...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Reddy, Kiran Jagannadha Udayaraju, Pamula Pamaiahgari, Venkata Pradeep Reddy Kumar, V. Dilip Walmart BentonvilleAR United States Srm University Department of Cse AP India Wendys International Llc OH United States SRKR Engineering College Department of CSE India

ISBN: (纸本)9798331523923

An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free;otherwise, it must schedule the same or relevant resource. This paper demonstrates a Resource-Aware Scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. The proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. The simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. The evaluation found that the RAS model is more suitable for cloud resource allocation. © 2025 IEEE.

关键词： Cloud computing Cost Optimization Resource Allocation Resource-Aware Scheduling Scheduling

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SimRank on Data Streams 31

SimRank on Data Streams

引用

31st IEEE international conference on High Performance computing, Data, and Analytics Workshops, HiPCW 2024

作者： Yajnik, Dhyan Ansari, Huzefa Aiyub Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

This paper introduces a novel approach that integrates machine learning techniques to optimize SimRank convergence on streaming data. Our approach utilizes the structural properties of the graph, such as edge features, to train a linear regression model that predicts SimRank values for newly added nodes. These predictions serve as effective initializations, ensuring faster convergence of the SimRank algorithm. By avoiding full recomputation and providing a new direction for research, our method offers a promising solution for similarity computation in evolving networks. © 2024 IEEE

关键词： Data streams

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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