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Optimizing Machine learning Algorithms for Temperature Forecasting and Energy Management Systems

Optimizing Machine Learning Algorithms for Temperature Forec...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra Sekhar, G. Kedhari Sailaja, P. MadhusudhanaRao, G. Srinivas, N. Sai Thrinath, B.V. Akash, G. Dept. of Computer Science Engg Aeronautical Engineering Hyd Visakhapatnam Dept of Computer Science Engg Institute Visakha Inst. of Engg. & Tech Dept of Electrical and Electronics Engg Vardhaman College of Engg Hyd. Dept of Electrical and Electronics Engg Mohan Babu University Tirupati Dept of Computer Science Engg Woxsen University Hyderabad

ISBN: (纸本)9798350373783

Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users through flexibility, availability, innovation, and environmental responsibility. These Data centers have observed tremendous growth, utilizing massive energy for computer resources and cooling systems. A combination of time series regression models, machine learning, and other optimization techniques may be used to develop more robust and adaptive resource management policies for complex data center environments. Additionally, continuous monitoring and feedback loops are essential to ensure that the models remain accurate and effective as conditions evolve. This paper studied existing machine learning algorithms and methods for temperature forecasting and adequate energy consumption for data centers. The investigations have been analyzed that the impact of energy consumption on computer resource workloads significantly improved, and Virtual Machines scheduling algorithms will optimize the forecasting of thermal effects. This paper also compares the regression model with the Machine learning algorithms, which will provide the best methodology for forecasting thermal effects and improving efficient energy. © 2024 IEEE.

关键词： Cloud computing Data Centres Index: Energy Efficiency Privacy Security Thermal Forecast Virtual Machines

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Application of Affective computing in Sentiment Analysis of English Writing in College Students 24

Application of Affective Computing in Sentiment Analysis of ...

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2024 international conference on Intelligent Education and Computer Technology, IECT 2024

作者： Xu, Hong College of Foreign Languages and Cultures Si Chuan University Sichuan Chengdu610000 China School of Foreign Languages Hubei Minzu University Hubei Enshi445000 China

ISBN: (纸本)9798400709920

Affective computing plays an important role in teaching and learning. The 47 college students' writing texts were deeply mined with the help of affective computing technology. The results showed that all the 47 texts had positive emotional tendency, 77.9% sentences of which were with positive emotional tendency, 7.46% sentences were with negative emotional tendency, and 14.64% sentences were with neutral emotional tendency;the positive and negative emotional words in college students' English writing texts are relatively simple, which emphasize the "friendship", "love"and "happiness";at the same time, there is no significant difference between students' English writing level and their emotional tendency in the text, but compared with the students with low scores, the students with high scores have more positive and negative emotional words, richer and deeper in the cognition of emotion. It is necessary to strengthen emotional education and carry out individualized, intelligent and effective educational reform according to students' different emotional tendencies and cognition. © 2024 ACM.

关键词： Students

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Optimizing Rose Leaf Disease Detection Performance via Explainable Transfer learning: A Comparative Analysis

Optimizing Rose Leaf Disease Detection Performance via Expla...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Solanki, Vikas Ahuja, Rakesh Khullar, Vikas Thapliyal, Somesh Chitkara University Institute of Engineering and Technology Punjab India Hemvati Nandan Bahuguna Garhal University Department of Pharmaceutical Sciences Uttarakhand India

ISBN: (纸本)9798350378092

This paper advocates a novel method for identifying disorders in rose leaves by applying transfer learning techniques. The methodology includes data collecting and preparation steps like reshaping, gray scaling, resizing, and augmentation. Initially, a basic Convolutional Neural Network (CNN) is utilized, followed by pretrained transfer learning techniques on ImageNet. Specifically, DenseNet201, EfficientNetB3, InceptionResNetV2, and MobileNetV2 are used. The analysis is performed based on categorical losses, recall, accuracy, and precision. It is evident from comparing the experimental results to the CNN model's baseline that transfer learning greatly increases accuracy, precision, and recall. Applying CNN transfer learning models to the DenseNet201 architecture produces the best results, with accuracy of 94.41%, precision of 96.18%, recall of 92.77%, and validation accuracy of 98.91%. The outcome of DenseNet201is also visually analyzed through layer wise explainability. Results show the effectiveness of transfer learning and how its improving the CNN model's ability to diagnose diseases in rose leaves. Proposed study particularly promotes automated disease identification in plants in the field of rose leaf diseases. The findings offer valuable information for managing plant health and agricultural activities in future. © 2024 IEEE.

关键词： Convolutional neural networks

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An Empirical Study on the Relationship Between University Students’Participation in Online Peer Assessment and learning Outcomes 24

An Empirical Study on the Relationship Between University St...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Chen, Jianqin Yang, Ying School of Information Yunnan Normal University Yunnan Kunming China

ISBN: (纸本)9798400717468

Online peer feedback is widely recognized in the pedagogical community as an effective approach to fostering communication and teamwork, both of which are essential for enhancing student learning outcomes. However, low student participation and inconsistent feedback quality limit its practical effectiveness. This study examined three rounds of peer assessment activities in a university computing course to explore the relationship between students’ learning outcomes, the frequency of peer assessments, and the length and relevance of feedback comments. Using SPSS software, students’ overall engagement was quantitatively analyzed, while NetDraw and UCINET tools were used to create peer evaluation and interpersonal relationship matrices to visualize engagement and minimize the impact of interpersonal connections on evaluation outcomes. Additionally, content analysis was employed to investigate the link between academic performance and comment length. The findings show that the quality of online peer feedback significantly influences student engagement and is positively correlated with learning outcomes, making it a key factor in predicting performance and guiding effective interventions. Based on these insights, the study offers recommendations to enhance the effectiveness of online peer review. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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A Contemporary CNN Implanted Deep learning Framework for Crop Protection from Animal Intrusion in Farming Environment

A Contemporary CNN Implanted Deep Learning Framework for Cro...

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2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Pavitra, A.Rehash Rushmi Panimalar, K. Rajalakshmi, D. Chinnasamy, A. Srm Institute Of Science And Technology Data Science And Business Systems Chennai India

ISBN: (纸本)9798350364828

In general, monitoring wild animal behavior at reasonable outlay is necessary for both managing human wildlife conflicts and protecting wildlife thereby reliable method is needed to detect wild animals. On the other side, Indian farmers endure severe risks from pests, natural disasters and animal damage resulting decreases the production process. To inspect crops and prevent animals from roaming farmers find it difficult to hire security and their standard approaches tend to be highly effective. Therefore, by applying efficient Deep learning (DL) framework facilitate to develop Convolutional Neural Network CNN algorithm that can identify wild animal intrusion and provide an indication to end user. Further research highlights smart agriculture applications that significantly minimize losses in production by utilizing DL to make digital notifications that protect fields against mammalian attack. In addition to implementing into consideration the constraints imposed by the farming environment in terms of power supply and network connection, the proposed system is built on image processing platform that provide an optimal combination of efficiency, expense, and power consumption. Ultimately, significant advancement toward cutting-edge technology and sustainable crop protection offers farmers can able to minimize the negative effects of wildlife on the farming industry while reducing adverse ecological implications. © 2024 IEEE.

关键词： Deep learning

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Deep learning-Based Diabetic Retinopathy Detection Using ResNet34 Model

Deep Learning-Based Diabetic Retinopathy Detection Using Res...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Anugirba, K. Lal Raja Singh, R. Bethlahem Institute of Engineering Department of Computer Science and Engineering Tamil Nadu 629 157 India Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641 402 India

ISBN: (纸本)9798350333244

Using the APTOS 2019 dataset and ResNet-34 architecture, this study presents an approach utilizing deep learning for classification of retinal images. Diabetes-related retinopathy and age-related macular degeneration are among the leading causes of blindness, and early detection is crucial for effective treatment. Convolutional neural networks (CNNs) have been shown to extract meaningful features from retinal images and accurately classify them by severity. In this study, the model was trained and validated using high-quality retinal images with varying disease severity from the APTOS 2019 dataset. ResNet-34, a 34-layer deep learning model renowned for image recognition, was used in the experiment. The model was trained on Google Colab using the PyTorch deep learning framework, with a batch size of 32 and 20 epochs. The introduced method demonstrated robust performance across various evaluation metrics, such as exactitude, recall, harmonized metric, Area Under the Receiver Operating Characteristic Curve (AUC ROC), and correct classification ability. Results showed that the proposed method accurately predicted the target variable. The study also presents loss and accuracy plots, confusion matrices, and AUC ROC plots, which provide a comprehensive analysis of the performance of the model which can be taken as a suggestion for improvement. © 2023 IEEE.

关键词： Image classification

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Face Recognition Using openCV- use case on student attendance system 15

Face Recognition Using openCV- use case on student attendanc...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kamalapur, Anjan Kumar Srikanth, Begari Yadav, Asari Shivadeep Yashwanth, Bachhanaboni Duraivelu, Vivek B V Raju Institute of Technology Dept.of Computer Science Engineering Telangana Narsapur502313 India

ISBN: (纸本)9798350370249

This is a modern approach to student attendance management using face recognition technology, powered by advanced techniques including Convolutional Neural Network (CNN) and Haar Cascade frontalface default algorithm. By leveraging OpenCV and these cutting-edge face recognition methods, the system offers a reliable and efficient solution for tracking attendance in educational institutions. Real-time face detection and recognition, support for multiple face recognition, and smooth interaction with current attendance management systems are some of the key characteristics. Additionally, the system incorporates deep learning algorithms to enhance its accuracy and robustness in identifying students' faces. Through practical implementation, the project demonstrates the effectiveness of using OpenCV and deep learning for automated student attendance management. By providing accurate and efficient attendance tracking, while improving operational efficiency in educational settings, this innovative solution marks a significant advancement in modernizing attendance management practices. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Deep learning Face Detection Haar Cascade Open CV

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2023 6th international conference on Big Data and Education, ICBDE 2023

2023 6th International Conference on Big Data and Education,...

引用

6th international conference on Big Data and Education, ICBDE 2023

ISBN: (纸本)9798400708220

The proceedings contain 22 papers. The topics discussed include: visual analysis of online learning engagement of student based on CiteSpace;analysis on the function and promotion strategy of metaverse empowering online-merge-offline teaching;innovation and application of university research management in the context of big data;research on the construction method of social relationship among learners for collaborative learning in online education;the influence of emotional experience on learning effect in blended learning in post-epidemic era;application of case-based PBL teaching method in the teaching of network and service engineering;research on the innovative path of psychological healthy education of university students in the context of big data;and one pulse and multiple lines drive the ideological and political teaching practice of endogenous course in big data course.

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Classification of Type2-Diabetes Using an Ensemble Deep learning Model CNN-LSTM 14

Classification of Type2-Diabetes Using an Ensemble Deep Lear...

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14th international conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Saxena, Ritcha Verma, Priya Sharma, Vikas Sharma, Kapil Jiya University of Minnesota Department of Biomedical Sciences MN United States Biotium Company Landing Parkway United States Delhi Technological University Department of Information Technology Delhi India Delhi Technological University Delhi India

ISBN: (纸本)9798350344837

One of the common diseases that many individuals experience is diabetes. One way to think of diabetes is as a collection of physiological a condition where the body's blood glucose level is higher than the standard guidelines for treatment. Typical method to identify the blood samples are drawn to measure blood glucose levels, which is highly painful. The creation of a blood glucose prediction model is the solution for preventing diabetes and may regulate insulin levels. Here, we suggest a novel deep learning forecasting model for the precise diabetes classification by blood glucose level prediction. The proposed model makes use of long short-term memory (LSTM) in identifying both short- and long-term dependencies, as well as convolutional layers to extract meaningful knowledge and learn the internal representation of time-series data. In this research work proposed CNN-LSTM hybrid model's performance is tested against the most advanced deep learning and machine learning models currently in use. According to the preliminary experimental research, using LSTM layers in conjunction with extra convolutional layers could significantly improve forecasting ability. © 2024 IEEE.

关键词： Long short-term memory

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Interpretable Machine learning Algorithms for Interpretation of Clinical Data 15

Interpretable Machine Learning Algorithms for Interpretation...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Krishna, Gautham Pathak, Manoj Kumar Dheer, Meenakshi School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of English Jharkhand Jamshedpur India Vivekananda Global University Department of Agriculture Jaipur India

ISBN: (纸本)9798350370249

Interpretable machine learning (IML) algorithms use mathematical principles of machine learning to assist know-how, perception, and manipulation of scientific facts. Those algorithms aim to bridge the gap between clinicians' knowledge of the facts and the output of traditional predictive analytics. IML algorithms have proved suitable for diverse obligations related to records analysis and interpretation of scientific facts, inclusive of predicting medical responses to remedy, exploring medical pathways, predicting future medical outcomes, and clustering affected person populations. Numerous techniques may be employed while using IML algorithms, which include linear fashions, choice tree models, help vector machines, neural networks, and Bayesian networks. Those fashions are mixed with specific parameters, including feature selection and weighting that can be used to evaluate the importance of every characteristic in predictive models. Moreover, those models can be implemented for non-linear facts by using regularization techniques. One essential advantage of IML over traditional devices in getting to know algorithms is the capacity to interpret the version outputs. Through using interpretable facts visualizations consisting of bar plots, scatter plots, and heat maps, clinicians can take advantage of insights into the relationships among features and goal variables. © 2024 IEEE.

关键词： Algorithms Interpretable Mathematical Networks Scientific Variables

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