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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Arun, V. Shenbagavalli, P. Sridhar, T. Manivannan, B. Mahesh, T.R. Anitha, K. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India Karunya Institute of Technology and Sciences Division of Digital Sciences India Alliance University Alliance College of Engineering and Design Bangalore India Computer Science and Engineering Namakkal India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350359756

IoT (Internet of Things) technology enables the connectivity and communication of healthcare-related equipment, medical devices, and sensors. This connectivity enables better monitoring, data collecting, and analysis, which leads to better patient outcomes and more effective healthcare delivery. However, with the rising use of linked devices, it is critical to detect and prevent Cyberattacks in order to secure sensitive patient information. In order to tackle this problem, the article delves into the application of machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB), These algorithms were evaluated using the WUSTL EHMS2020 dataset and demonstrated the highest level of accuracy. © 2023 IEEE.

关键词： Internet of things

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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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Integrating Emotion Detection with Sentiment Analysis for Enhanced Text Interpretation

Integrating Emotion Detection with Sentiment Analysis for En...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Arpan Ghosh Naimish Pandey C. AshokKumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

The analysis of public sentiment on various social media platforms has become more important.. However, conventional sentiment analysis often struggle to capture intricate emotional nuances present in textual data.. This study proposes an innovative approach by integrating emotion detection with sentiment analysis to improve the interpretation of text.. A novel algorithm "BERT-DeepEmo" is proposed, combining the strong contextual modeling capabilities of Bidirectional Encoder Representations from Transformers (BERT) with a deep learning-based emotion recognition model (DeepEmo). Simulation analyses were performed to evaluate the effectiveness of the proposed algorithm compared to established approaches like BiLSTM-ECNN and RoBERTa-EDTA. Evaluation metrics including accuracy, precision, and mean absolute error were employed for comparison purposes. The results confirmed that BERT-DeepEmo enhances the overall quality of sentiment analysis while considering the emotional nuances in text. These findings highlight the proposed approach's potential to offer a more holistic and effective sentiment analysis framework, thereby facilitating informed decision-making and deeper insights across various applications. Future research work may explore into further integration with alternate process of emotional data, such as speech or visual inputs, to broaden the framework's applicability and efficacy.

关键词： Sentiment analysis Emotion recognition Adaptation models Visualization Accuracy Social networking (online) Bidirectional control

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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Computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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Computer Systems Science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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IoT-Based Smart Underground Cable Fault Detector 1

IoT-Based Smart Underground Cable Fault Detector

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1st IEEE International Conference on Communication engineering and Emerging Technologies, ICoCET 2024

作者： Jayachandran, Murugakummar Abdul Rani, Khairul Najmy Wahab, Alawiyah Abd Universiti Malaysia Perlis Faculty of Electronic Engineering & Technology Perlis Malaysia Universiti Malaysia Perlis Centre of Excellence Advanced Communication Engineering Faculty of Electronic Engineering & Technology Perlis Malaysia School of Computing College of Arts & Sciences Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798331504144

Underground high-voltage cable usage is expanding due to their resistance to pollutants, severe weather, heavy rain, and storms. However, these cables are prone to damage due to improper installation, poor jointing, and potential harm from third-party activities such as curb edging or trenching. Additionally, environmental factors, abrasion, and animal threats further complicate the process of locating and repairing faults. This project introduces a diagnosis system utilizing the Internet of Things (IoT) to identify and locate faults in underground cables. By enabling faster fault detection, the system aims to reduce the time, money, and effort associated with repairs. The IoT-based solution utilizes a set of voltage dividers mounted on the cable to detect faults. When a fault occurs due to a shortening between two cables, a voltage is generated, which is then detected by an ESP32 microcontroller. This microcontroller notifies the user by providing the fault location in kilometers. The gathered data is displayed on a liquid crystal display (LCD) and transmitted online via a Wi-Fi module to the Blynk cloud database for storage and further analysis. © 2024 IEEE.

关键词： Liquid crystal displays

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light Controlling and Monitoring System using Intelligent Sensors 9

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Saranya, G. Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India

ISBN: (纸本)9798350365092

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability. © 2024 IEEE.

关键词： Internet of things

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Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

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Computer Systems Science & engineering 2022年第8期42卷 545-560页

作者： R.Saravana Ram M.Vinoth Kumar N.Krishnamoorthy A.Baseera D.Mansoor Hussain N.Susila Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing *** play a vital role in emerging technologies of the 20th *** of sensors in needed locations is a very serious *** environment is home to every living being in the *** growth of industries after the industrial revolution increased pollution across the *** to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are *** interesting and challenging task is choosing the place to fit the *** meta-heuristic techniques have been introduced in node *** intelligent algorithms have proven their efficiency in many studies on localization *** this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor ***,our work aims at selecting industrial areas in the sensed *** use random forest regression methodology to select the polluted ***,the elephant herding algorithm is used in sensor node *** two algorithms are combined to produce the best standard result in localizing the sensor *** check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with *** results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer *** can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the *** proposed method achieves a lower root mean square value with 0.06 to 0.08 for lo

关键词： Wireless sensor networks node localization industrial-centric approach random forest regression elephant herding optimization swarm intelligence pollution

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Enhancing Asthma Diagnosis Through Machine Learning: A Stacking Ensemble Approach

Enhancing Asthma Diagnosis Through Machine Learning: A Stack...

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Women in computing (InCoWoCo), International Conference for

作者： Manas Goel Manya Gupta C. Jothikumar S. Shanmugam C. Ashokkumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Asthma diagnosis remains a major clinical concern since it has diverse symptoms and is caused by various contributory causes. Machine learning was applied in this study to solve these complications in order to analyze such a broad dataset of 2,392 patients with the capture of important variables such as demographics, environmental exposures, allergies, medical history, and clinical indicators. Several machine learning models were implemented, including those such as Random Forest, XGBoost, Decision Tree, LGBM, CatBoost, and AdaBoost. Next, the stacking ensemble strategy was taken to further optimize predictive performance, using the Random Forest model meta-classifier. Our approach proves that stable ensemble learning takes place with an amazing 97% accuracy rate when it comes to difficult clinical data. Recall, precision, and F1-score represent some of the most important measures for model performance evaluation. These results might point out the machine learning potential in improving asthma diagnosis accuracy and might be helpful for a doctor in coping with this complicated disease.

关键词： Performance evaluation Accuracy Stacking Medical services Predictive models Ensemble learning History Random forests Medical diagnostic imaging Asthma

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Movie Recommendation Algorithm based on Sentiment Analysis and Topic Model 7

Movie Recommendation Algorithm based on Sentiment Analysis a...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Xie, Jin Zolkipli, Mohamad Fadli Mohsin, Mohamad Farhan Mohamad Qiu, Tianyu Liu, Feng Hou, Meng College of Electronic and Information Engineering Hubei University of Automotive Technology Shiyan China School of Computing Universiti Utara Malaysia Sintok Malaysia College of Information engineering and computer Hebei Finance University Baoding China College of Big Data Guizhou Vocational College of Industry and Commerce Guiyang China

ISBN: (纸本)9798331519513

Traditional recommendation algorithms often face challenges such as data sparsity and a lack of diversity in recommendation results. To address these issues, this paper proposes a novel recommendation algorithm that integrates graph neural networks (GNN) and topic modeling. Initially, a topic model is employed to determine the topic distribution of sentences in reviews, connecting sentences with the same topic. Additionally, a gating unit is introduced to regulate the information flow between the layers of the GNN. To capture the internal and external causes of sentiment changes more effectively, differences in character information are considered in the edge types. This approach enriches both the user interest model and the product feature model based on sentiment density. In the experimental performance on the Douban movie review dataset, the precision, recall and F-measure of the new algorithm are 91.25%, 89.27% and 89.74%, respectively. © 2024 IEEE.

关键词： Neural network models

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