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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gowda, Prashant Naik, Sakshi Kanakia, Prof. Harshil Masters of Computer Application Sardar Patel Institute of Technology Mumbai India

ISBN: (纸本)9798350327533

SpireMetrics, a web-based platform, facilitates the collection and analysis of student feedback on current semester subjects, assessing faculty performance in areas like subject knowledge and doubt resolution. Ensuring anonymity, the system promotes honest feedback and features secure logins for administrators and faculty. Administrators create feedback forms specifying subjects and faculty, while faculty access and review feedback, enhancing transparency. Angular and *** enable data visualization, offering a comprehensive view. Additionally, machine learning, specifically the BERT, Bidirectional Encoder Representations from Transformers model, is integrated to predict sentiment in the feedback, distinguishing between positive and negative responses. This study explores the SpireMetrics architecture, implementation, and outcomes, highlighting the usefulness of the tool for educational institutions. The findings reveal the project's potential to improve the educational experience and the accuracy of sentiment analysis. This research paves way for future developments in educational feedback systems and sentiment analysis in academic settings. © 2024 IEEE.

关键词： BERT Model Data Visualization Educational Feedback Sentiment Analysis Student Feedback

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Cloud service price prediction using Machine Learning Algorithm with API in the case of Amazon Web Services and Microsoft Azure

Cloud service price prediction using Machine Learning Algori...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Dhayanandan, B. Rajeev, R. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350375237

Due to the availability of highly scalable and reasonably priced infrastructure, software, and platform services, many enterprises are moving their on-premise artificial intelligence workloads to the cloud. The study focuses on the K-Nearest Neighbors (KNN) algorithm and uses a quantitative method to build and assess a predictive model for cloud service price prediction. Pre-processed historical pricing data from Microsoft Azure and AWS is gathered, together with variables that affect the cost of cloud services. In order to generate models, the KNN algorithm is used, and an API is made to integrate real-time price predictions. Evaluation measures (MAE, MSE, and RMSE) are employed. Because cloud pricing is unpredictable, the suggested method addresses this dynamic by improving accessibility and adaptability. Findings from exploratory data analysis and features of the API show how effective the system is. This contribution involves providing customised solutions for each customer while taking ethical and practical constraints into account. All things considered, the integrated approach is a major improvement in cloud service pricing prediction, allowing for 'well-informed' budgetary and resource allocation decisions. © 2024 IEEE.

关键词： Costs

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Techniques for Fine-Grained Analysis of Scientific and Technological Intelligence 24

Techniques for Fine-Grained Analysis of Scientific and Techn...

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2nd International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2024

作者： Zhao, Xiaoyan He, Yao Gao, Yankun Luo, Xu Ke, Wenjun Beijing Institude of Computer Technology and Application China SouthEast University China

ISBN: (纸本)9798400716898

In today's world, scientific and technological intelligence information presents challenges such as insufficient annotated samples in interdisciplinary and specialized fields, the continuous emergence of new technologies, and complex interwoven dependencies of technical points. These aspects pose significant obstacles for the fine-grained acquisition and analysis of scientific and technological intelligence. To address these issues, this paper focuses on three areas: domain-aware generation of scientific and technological intelligence samples, extraction of intelligence entity relationships based on continual learning, and mining of technological dependency chains using causal graphs. This research aims to provide foundational techniques and algorithms for the acquisition and analysis of fine-grained scientific and technological intelligence, thereby enhancing our nation's capability in technological trend assessment and development planning in the context of global competition. © 2024 ACM.

关键词： dependency chain mining

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A Receiving Gain Monitoring and Dynamic Regulation Method for High Frequency RFID System with Dense Tags 5

A Receiving Gain Monitoring and Dynamic Regulation Method fo...

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5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

作者： Chai, Jia Ma, Chengzhen Ruan, Zhicheng Li, Yang Qin, Lei Ren, Kaiqi Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9798350336030

Radio Frequency Identification (RFID) system is widely used in archives management, intelligent warehousing, and other industries due to the advantages of insensitivity to liquid, controllable reading range and high reading density. However, in the application scenario of dense tags inventory, the position of the tags relative to the readers is different and dense tags are coupled with each other, so the intensity range of the tag response is wide. To ensure that all tags can be inventoried exactly, the readers are required to have a high dynamic range. In this paper, considering the relationship between receiving gain and anti-collision flow, a method of receiving gain monitoring and dynamic regulation for high-frequency RFID systems is designed. The experimental results show that the proposed method can effectively improve the dynamic range of the reader on the premise of ensuring the efficiency of inventory. © 2023 IEEE.

关键词： Digital signal processing

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Secure Online Product Recommendation System Utilizing Blockchain with SHA-256 Hash Generation 15

Secure Online Product Recommendation System Utilizing Blockc...

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

作者： Kumar, R. Ajith Janit, R. Sophia Department of computer application Hindustan institute of technology and science Chennai India

ISBN: (纸本)9798331300579

Blockchain-based product tracking creates anirreversible and permanent ledger of a product's whole lifespan, from conception to final delivery, revolutionizing supply chain management. This technique creates a transparent and safe system for tracking and validating product information all the way through the manufacturing process by utilizing blockchain technology. Manufacturers and distributors can ensure transparency and authenticity at every level of the supply chain by using blockchain technology to trace every step-in detail. Because blockchain is unchangeable and tamper-proof, it is the perfect tool for preventing fake goods and guaranteeing product integrity. Blockchain lowers the risk of fraud and manipulation by recording every movement and transaction in the supply chain in a decentralized, transparent manner. Consumer trust is strengthened by this increased degree of responsibility and openness, which also protects the authenticity and quality of products. Customers feel more confident when blockchain technology is used since they can be sure of the origin and caliber of the goods they buy. Improved traceability and transparency empower customers to make knowledgeable decisions and protect themselves from the dangers of fake goods. Additionally, blockchain strengthens brand loyalty and integrity by creating a marketplace that is transparent and trusting, protecting companies from the negative impacts of counterfeiting. © Grenze Scientific Society, 2024.

关键词： Blockchain

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Robust Hand Gesture Recognition Based On computer Vision

Robust Hand Gesture Recognition Based On Computer Vision

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Tharun, R. Lakshmi, I. Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350375237

Research in this field has been provoked by the developing interest in human-computer interaction. Also, normal client interfacing and encounters (NUI/NUX) that make use of a user's discourse and developments have been done. In NUI/NUX, a discourse or signal acknowledgment calculation is required. Tragically, numerous acknowledgment calculations have disadvantages since of their complex engineering and long preparation times. Pre-processing, normalization, and extraction must therefore be carried out. In this thought, we planned and actualized a hand-mouse interface that utilizes Kinect to extract a user's physical highlights in genuine time, showing a special concept known as a 'virtual screen.' The hand mouse may work in a virtual zone, which is much appreciated for this virtual show. It is conceivable to outline the arrangements on the virtual show precisely to those on the genuine screen. Based on the virtual screen concept, a hand-mouse interface jams the exceptionally instinctive nature of the past;consider upgrading the accuracy of mouse operations. We tried the precision and value of theinterface with fifty volunteers, extending in age from 15 to 50. 84D44 of members in this test of instinct were able to utilize them in less than a miniature. The precision try too illustrated the accuracy of the mouse's tap, drag, and double-click capacities. Typically, a good illustration of an interface will be utilized in the future for manual framework control. © 2024 IEEE.

关键词： computer vision

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Machine Learning Techniques for Identifying Distressed Individuals on Social Media - A Survey

Machine Learning Techniques for Identifying Distressed Indiv...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Sadhukhan, Ankan Samanta, Sreejani Pasari, Abhijit Samui, Sayantika Mandal, Dipayan Ghosh, Sandipta Guria, Mrinmoy Pal, Debrupa Computer Science and Engineering Narula Institute of Technology Kolkata India Computer Application Narula Institute of Technology Kolkata India

ISBN: (纸本)9798350378047

The pervasive presence of social media platforms has turned them into reservoirs of human emotions, providing a distinctive perspective into the intricate tapestry of digital sentiments. This research delves into the realm of emotional upheaval prevalent in online spaces, aiming to uncover signs of distress through the utilization of machine learning algorithms. By scrutinizing extensive volumes of social media data, this survey seeks to establish a pioneering framework for predicting the identification of individuals undergoing emotional distress. Many papers are published on machine learning approaches over the past few decades. In this paper, we provide a comprehensive survey of all best machine learning approaches in the current year to study or identify distress calls on social video through sentiment and media analysis. Furthermore, the challenges and benefits of using Machine Learning, along with its shortcomings, are summarized. Finally, open issues and their consequences for further research in the field of identifying individuals who are mentally depressed on social media are presented. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of Transfer Learning algorithm in predicting Alzheimer's disease 1

Comparison of Transfer Learning algorithm in predicting Alzh...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Yashodhar, Archana Kini, Shashidhar Department of Computer Science and Application Mangaluru India Srinivas Institute of Technology Master of Computer Application Mangaluru India

ISBN: (纸本)9798350348057

Many neurogenerative diseases affect today's generation. The causes of this condition are unclear. Alzheimer's disease is one of that diseases, and elderly peoples suffer a lot from this. In this article, we applied several deep learning transfer learning approaches to categorize distinct phases of Alzheimer's disease. Different measures are used to assess each strategy, which leads to better results. © 2023 IEEE.

关键词： Deep learning

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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Construction and application of Aerospace Quality Knowledge Graph 4

Construction and Application of Aerospace Quality Knowledge ...

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4th International Conference on Signal Processing and computer Science, SPCS 2023

作者： Zhang, Chenyue Dong, Fangxu Long, Qinghua Beijing Institute of Computer Technology and Application Beijing100854 China Beijing Radio Measurement Institute Beijing100854 China

ISBN: (纸本)9781510672505

Aiming at the huge and discrete storage of quality problem data in aerospace model quality management, a method for improving the efficiency of finding aerospace quality information by constructing a knowledge graph of aerospace quality and applying a question-answering system is proposed. The graph construction adopts a top-down construction method. First, the concept-attribute-relationship ontology model of aerospace quality knowledge is constructed. Then, based on combining expert knowledge to design the ontology concept of aerospace quality knowledge, the data to be processed is pre-processed by BERT. The training model is combined with deep learning technologies such as BiLSTM-CRF and self-attention mechanism to realize independent knowledge extraction, and then the Dice coefficient combined with the weighted average method is used to fuse the entity-relationship extraction results. Finally, based on the constructed knowledge graph, to improve the accuracy of semantic template matching, a natural language question semantic classification process model based on a weighted naive Bayesian classifier is established, which can further realize intelligent question answering. The results of the paper show that the intelligent application of knowledge graphs in the field of aerospace quality has a good prospect. © 2023 SPIE.

关键词： Deep learning

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