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A data-driven Predictive Approach Using Custom and Regression algorithms for Estimating Student’s Performance

A Data-driven Predictive Approach Using Custom and Regressio...

引用

Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： Rajin Gangadharan K. Vijayakumar P. Nithya T. Siva Department of Computer Science and Engineering OMR St. Joseph’s Institute of Technology Chennai India SRM Arts and Science College Kattankulathur Chennai India Department of Electronics and Communication Engineering OMR St. Joseph’s Institute of Technology Chennai India

Efficient approaches to estimating and understanding student performance are paramount in the dynamic landscape of higher education. This paper introduces "Adata-driven Predictive ApproachUsing Custom and Regression Models for Estimating Student’s Performance," aiming to provide educators with valuable insights into academic outcomes. By integrating specific academic data sources and employing regression analysis, machine learning algorithms, and other data-driven methodologies, educators can gain actionable insights into student performance trends through predictive modeling. The paper delves into the technical complexities of the strategy, outlining methodologies and presenting practical implementations through illustrative examples and case studies. The ethical considerations of data usage and privacy are carefully investigated, emphasizing responsible implementation in the educational context. Pilot implementations in real-world educational settings are explored, shedding light on the strategy’s efficacy and potential impact on educators and students. This study represents a significant advancement in using data-driven strategies to estimate student performance in higher education, envisioning a future where educators are equipped with valuable tools to understand and support each student’s academic journey comprehensively.

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Scene Understanding: Do we even need Labels and data ?

Scene Understanding: Do we even need Labels and Data ?

引用

IEEE international conference on Intelligent Computer Communication and processing (ICCP)

作者： Raoul De Charette Astra Team at INRIA Paris France

Despite their ever growing sizes, computer vision datasets are doomed to reflect only a tiny fraction of our world. The induced biases raise ethical issues and show that blind reliance on data can have critical outcomes when it comes to applications like autonomous driving. In this talk, we will investigate visual scene understanding in the era of large-scale datasets, self-supervised learning and LLMs. Following our recently published research we will question our use of machine learning and data for real and open world scene understanding by navigating through these questions: Are we making the best use of existing datasets ? Can we benefit from more knowledge priors ? Can vision algorithms perform in the unknown open-world?

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Cloud computing Infrastructure in Smart Home Devices

Cloud Computing Infrastructure in Smart Home Devices

引用

international conference on Image Information processing

作者： Rajarshi Roy Nidhi Sharma Dept of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Cloud computing has transformed how we engage with technology. Computer users now have easy access to computing resources via the internet. This inclination is not limited to smart home devices. Smart home devices have become more dominant by means and the use of technology. Internet of Things (IoT) takes grown the popularity, makes our daily lives easier and more efficient. Cloud computing technology provides a chance to increase the performance, usefulness of smart home devices with integration in smart home gadgets. The author has worked and emphasizes the assistances of cloud computing, like as scalability, cost-efficiency, greater storage capacity, along with covering the drawbacks, such as security threats, data privacy concerns, and latency issues. Recent research studies imply that cloud computing can considerably progress the performance of smart home devices by providing greater storage, processing power, and data analytics capabilities. The possible risks associated with cloud computing must be carefully handled to maintain the security and privacy of users’ data has discussed in the paper.

关键词： Performance evaluation Cloud computing data privacy data analysis Costs Scalability Smart homes

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Cursive Hand writing Recognition Using Neural Networks with VGG-16

Cursive Hand writing Recognition Using Neural Networks with ...

引用

Advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： Anushka Anil Rangari Swagat Das Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

Recognition of cursive handwritten text from photographs is a method for finding handwritten text. Identification is difficult because every author has a distinctive writing style. The proposed work uses cutting-edge technologies including RNN, KNN, MLP, and CRNN twith VGG16 to push the boundaries of character and handwriting recognition. Additionally, it compares neural networks to CNNwith VGG16 and finds that neural networks are more accurate. In the first stage, image acquisition, the scanned image is acquired along with its normalization, feature extraction, and segmentation. Additionally, it combines with the most effective feature extraction technique to improve the accuracy of our model. It also compares the accuracy with six uniquepretreat. approach, which are image segmentation image data generator data augmentation closed loop pre-processing, sigmoid Stretching and geometric *** provides the highest accuracy (98.36%) when using data augmentation. CNN has the highest accuracy (95.25%) when using picture segmentation. The third experiment's results for the image data generator show that CNN produced the best results (92.47%), while the highest model, RNN, gave closed loop processing accuracy of 92.20%. a change in geometry KNN has the highest accuracy at 95.39%.

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Enhancing data Security in the Cloud Platform by Encrypting the Used Key Dynamically

Enhancing Data Security in the Cloud Platform by Encrypting ...

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Smart computing and Application (ICSCA), international conference on

作者： Lamya Khalid Abdulaziz Alsumair Kawther A. Aldhlan Information and Computer Science Department College of Engineering and Computer Science University of Ha'il

The smart city is a basic part of the future. It has many advantages on different levels. However, the most concerning aspect is the sensitivity of its data, which faces several risks that could harm individuals' lives. This paper focused on the shortages in the preservation and handling of the encryption key. In addition, to propose an approach that aims to enhance the application of encryption in the cloud platform, which is one of the technologies that are used to store the data of smart city, since most studies indicated that encryption is the most recommended security mechanism. The current paper provided a brief description of the smart city and its related topics. The experimental method was adapted, and the experiment of the study included two stages, which are testing and building. And through the experiment, the performance of AES (Advanced Encryption Standard) and Blowfish encryption algorithms was investigated by applying them on multiple Word documents. Eventually, it was found that AES had consumed 54%, 47%, and 52% of the consumed time by Blowfish in the first round, and 32%, 44%, and 39% in the second round, respectively, in each document encryption process. Hence, AES was employed in the proposed approach, which suggests encrypting the stored data in the cloud, then encrypting the used key dynamically by using a constantly changing generated key based on a specified period of time. By adapting the encryption with a Dynamic Key (DK), the level of data security increases, which was confirmed by a variety of reviewed literature.

关键词： Sensitivity Smart cities Heuristic algorithms Buildings Encryption Standards Faces

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Research on the Collaborative Interaction between Electricity and computing Power for Green Development-Taking data Centers as an Example

Research on the Collaborative Interaction between Electricit...

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Signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Wei Tang Chenglong Zhang Jinhui Duan Zhenda Hu State Grid Energy Research Institute Co. Ltd. Beijing China Center of Energy and Research Power Economic Research Institute of State Grid Fujian Electric Power Company Fuzhou China

The allocation of electricity and computing resources is an important factor affecting economic development and social stability. Through research on the coordinated operation of computing power and electricity, resource allocation can be optimized, resource utilization efficiency can be improved, investment in power grid construction can be reduced, fossil energy consumption and carbon emissions can be reduced, and low-carbon and green development of electricity can be promoted. This article takes data centers as an example to study and analyze the planning and operation related issues of computing power collaboration. The focus is on the perspective of power supply and demand balance, and proposes a theoretical method for data centers to participate in demand response and achieve collaborative interaction with the power grid. Firstly, the composition of the adjustable load potential of the data center and the mechanism of participating in grid interaction were analyzed. Then, a data center energy supply and load energy consumption model is established. Finally, considering delay sensitive loads and delay tolerant loads, based on the time scheduling mechanism, spatial scheduling mechanism, and server optimization management mechanism of the data center, a potential calculation model for the participation of the data center in power demand response is proposed. This study provides theoretical support for the inclusion of the data center in power grid scheduling.

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Application Research of Neural Network-based data Assessment and Prediction Method in Statistical Analysis

Application Research of Neural Network-based Data Assessment...

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6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Xiaolian Li Kun Xu Xiaoqian Men Zhe Lin Yixuan Zhao Daiming Wei PetroChina Research Institute of Petroleum Exploration & Development Beijing

This article introduces the application of neural networks in evaluation and prediction tasks. Compared with traditional statistical methods and manual experience, neural networks have the characteristics of automatically learning based on data, can extract effective features from massive data, and achieve high accuracy prediction and complex pattern recognition of data through modeling and learning of deep abstract representations. Neural networks are widely used in petroleum data prediction, financial risk assessment, social network prediction, medical diagnosis, and other fields. This article aims to improve the accuracy and efficiency of prediction and decision-making based on the research of statistical evaluation and prediction methods based on neural networks. In addition, this article also introduces the indicator system and evaluation and prediction construction standards. The indicator system mainly describes the relationship between maintenance and indicators and their respective main attributes, including indicators, dimensions, measurement units, and unit indicators. The evaluation and prediction construction standards include production and operation data prediction and data quality prediction. The prediction platform provides various functions to support data prediction, such as prediction indicator management, prediction algorithm management, prediction algorithm simulation, prediction rule engine, production and operation warning, data quality prediction, prediction information release, and prediction problem management. The research and application of this article have important significance for promoting data mining, image recognition, natural language processing, and other fields.

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Speech Emotion Recognition using Dialogue Emotion Decoder and CNN Classifier

Speech Emotion Recognition using Dialogue Emotion Decoder an...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Sarthak N Atkar Rahul Agrawal Chetan Dhule Nekita Chavan Morris Pranay Saraf Kamlesh Kalbande Data Science IoT and Cyber Security (DIC) G H Raisoni College of Engineering Nagpur India Department of Data Science IoT and Cyber Security (DIC) G H Raisoni College of Engineering Nagpur India

Recently, everywhere in the world for the purpose of recognizing the emotions research are carried out. Understanding a person's true state at the time they are uttering words is the first stage of determining their emotions from their speech. One's voice pitch also determines the state of the emotion. Eventually, in this paper the emotions are extracted in five forms (Excite, anger, happiness, neutrality, and sadness). After the pre-processing of the input speech data the dataset is ready for the feature extraction using Dialogue Emotion Decoder (DED) and CNN classifier to give the output of the utterances in the form of emotions.

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An Energy Efficient Artificial Intelligence Based Innovation Detection for Complex data Communication Model

An Energy Efficient Artificial Intelligence Based Innovation...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： K. S Arvind G Sindhu Madhuri Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India

The Artificial Intelligence (AI) based innovation detection model for complex data communication model is a revolutionary approach to identifying and exploiting opportunities for innovation in data communication systems. This model is based on the principle of AI-assisted data mining, which allows for the automated detection of patterns and correlations in data sets. By applying AI-based algorithms, this model can identify patterns or relationships between elements of large data sets which may not be obvious to human analysts. The AI-based innovation detection model for complex data communication model is designed to help organizations identify areas in which they can capitalize on opportunities for innovation. This model can identify complex patterns or relationships between data-driven variables such as customer behavior, product performance, and market trends. By leveraging the data-driven insights, organizations can create new products or services that are tailored to their customer’s needs and desires. The AI-based innovation detection model for complex data communication model can be used to identify opportunities for innovation in a variety of industries. For example, the model can be used to identify potential opportunities for new product development in the healthcare sector. By analyzing patient records, AI-based algorithms can uncover patterns or correlations between different types of patient data that may not be obvious to human analysts. Additionally, this model can also be used in the retail sector to identify potential opportunities for developing new products or services that are more tailored to customer needs and desires.

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Application Research of Computer Virtual Reality Technology in Big data Information Face Recognition System

Application Research of Computer Virtual Reality Technology ...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Xin Yu Yuan Li Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

This paper studies the edge-cloud collaborative face recognition technology. This project intends to integrate cloud computing technology with boundary computing technology, breaking through the limitations of traditional algorithms on network environment and expanding the scope of application of algorithms. This improves the efficiency of the user. The image feature extraction algorithm based on Resent is studied, and the ResNet34 network is perfected. By introducing the face loss function of ArcFace to guide the training of the network, the network can better understand the corner features of the face. In the boundary aspect, this project intends to study the lightweight Resent structural model based on depth-separable volume to solve the problem of limited computation and memory. The SResNet model based on the lightweight Resent structure is studied. The effectiveness and real-time performance of this method are verified by the facial recognition experiments on edge cloud collaboration.

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