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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Premasudha, B.G. Rampalli, Vijayalaxmi Siddaganga Institute of Technology Department of Master of Computer Application Tumkur India

ISBN: (纸本)9798350376517

In recent years, sentiment analysis has become an important tool for understanding what people think and feel about different topics, especially in education. This study looks at how well Long Short Term Memory (LSTM) networks work compared to traditional methods like Support Vector Machines (SVM) and Logistic Regression for classifying sentiment in text. By analyzing a dataset of 18,901 college reviews, which includes reviews sourced from Kaggle and additional reviews for improved dataset balance, this research evaluates how well LSTM networks can detect nuanced sentiments compared to more conventional models. The study includes steps like cleaning the data, testing and fine-tuning several machine learning methods, and measuring how accurately they capture the contextual nuances in the reviews. Findings reveal that LSTM networks outperform traditional methods, offering enhanced accuracy in capturing the emotional depth and context of the text. This study provides valuable insights into the use of advanced neural network models for sentiment analysis, highlighting their ability to improve the interpretation of user feedback. The findings suggest that incorporating LSTM networks into educational feedback systems could result in more accurate sentiment classification and contribute to improved decision-making processes. © 2024 IEEE.

关键词： Support vector regression

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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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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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SpireMetrics: An Integrated Approach for Student Feedback Analysis and Sentiment Prediction 2

SpireMetrics: An Integrated Approach for Student Feedback An...

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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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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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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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A Survey and Classification on Recommendation Systems 2nd

A Survey and Classification on Recommendation Systems

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2nd International Conference on Big Data, Machine Learning, and applications, BigDML 2021

作者： Sharma, Manika Mittal, Raman Bharati, Ambuj Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Application National Institute of Technology Haryana Kurukshetra136119 India

ISBN: (纸本)9789819934805

In today’s modern world, the data is growing exponentially and the traditional systems are not able to fulfil the user’s requirements. To fulfil the needs of the users, various companies like Amazon, Netflix, etc. are using recommender systems which recommend content or various type of data on the basis of the user’s previous activities and interactions with the system. In the recommender system, mainly three approaches are present, i.e., content-based, collaborative filtering and knowledge-based approaches. Due to their wide applicability, recommender systems have become an area of active research and in this context, this paper furnishes a survey and comparative discussion of existing approaches. The survey draws a conclusion on how different recommendation techniques are cooperating with today’s growing technology trends and also discusses the challenges faced by them. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

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2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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