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检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

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FloraMediVision: A Medicinal Plant Leaf Identification System using computer Vision 6

FloraMediVision: A Medicinal Plant Leaf Identification Syste...

引用

6th IEEE international conference on advances in science and Technology, ICAST 2023

作者： Patil, Pradnya Kulkarni, Kaustubh Sonkar, Minal Deshmukh, Priyanka K J Somaiya Institute of Technology Computer Engineering Dept Mumbai India

ISBN: (纸本)9798350359817

The use of medicinal plants has been a fundamental aspect of traditional and alternative medicine for centuries, offering a vast reservoir of natural remedies for various health conditions. However, the accurate identification of these plants is a crucial step in ensuring their safe and effective use and it requires human expertise. In recent years, advances in computer vision and machine learning have paved the way for the development of innovative solutions to address this challenge. Using this convergence in technologies, a large number of features of leaf images have been extracted and fed to machine learning algorithms forming a cutting-edge system - the Medicinal Plant Leaf Identification System using computer Vision - which leverages technology to revolutionize the identification of medicinal plants based on their leaves. © 2023 IEEE.

关键词： computer vision

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Artificial Intelligence in Next Generation IoT Infrastructure: Challenges and Opportunities 1

Artificial Intelligence in Next Generation IoT Infrastructur...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arabelli, Rajeshwarrao Chotaliya, Dhwanit K. Gadhave, Rajashree Tukaram Haritha, B. Kasireddy, Lakshmi Chandrakanth Soni, Mukesh School of Engineering SR University Department of Electronics and Communications Engineering Warangal India GEC-Rajkot Department of Instrumentation and Control Engineering Gujarat Rajkot360005 India Pillai HOC College of Engineering and Technology Rasayani Tal. Khalapur MH 410207 India St. martin's Engineering College Department of ECE Hyderabad India ThoughtSpot Inc Enterprise DevSecOps Architect R&D - Engineering FranklinTN United States Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331518578

Numerous facets of the public realm could be revolutionised by the convergence of the Internet of Things (the Internet of Things) and Artificial Intelligence (AI). This project developed a based-on data predictive maintenance system for manufacturing production lines. One of these anticipated improvements that can be included into the factory's production system is 'AI of Things (AIoT),' which refers to the use of the IoT to carry out intelligent tasks with the aid of AI integration. By using the information created by IoT sensors progressively, the system utilizes machine learning (ML), a part of AI, to recognize expected blunders preceding they happen. The viability of the innovation was surveyed utilizing IoT information from genuine assembling frameworks, and the results of the appraisal demonstrated that the prescient support structure was effective in distinguishing potential failure indicators and could assist with forestalling some production stops. Comparing the performance of ML techniques revealed that frameworks of the bagging ensemble technique Random Forest and the boosting technique XGBoost appeared to outperform each of the techniques in the assessment. The factory's production system now uses the best-performing ML techniques from the present research. © 2025 IEEE.

关键词： Predictive maintenance

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A Comprehensive Survey of Deep Learning Models for Legal Document Summarization 15

A Comprehensive Survey of Deep Learning Models for Legal Doc...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Reddy, Kancharla Bharath Jayabharathy, J. Puducherry Technological University Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798331300579

Legal document Summarization poses a difficult challenge because of the complexity and diversity of legal language and *** recent years, deep learning models have shown promising results in summarizing legal *** article examines the challenges of legal document summarization, reviews the state-of-the-art deep learning models, and identifies research gaps in this *** future research directions and applications of legal document summarization using deep learning models are also *** paper is a valuable resource for researchers and practitioners interested in legal document summarization using deep learning models. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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Development of Learning Management Systems and Effects in Education: A Comprehensive Review 1

Development of Learning Management Systems and Effects in Ed...

引用

1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Saxena, Saksham Sharma, Deepti Saxena, Lokesh Singh, Aayush Sachdeva, Vrinda Department of Computer Science and Engineering India

ISBN: (纸本)9798350356816

This study examines the ways in which Learning Management Systems (LMS) have become an essential component of contemporary education, influencing both teacher effectiveness and student learning. It looks at the origins of LMS, its main characteristics, advantages and disadvantages, and how it has affected different learning settings. The report also looks at prospective areas for future research in the developing LMS sector, as well as emerging trends and opportunities. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis and Classification of Heart Disease and COVID 19 Using Electrocardiogram and Deep Learning

Analysis and Classification of Heart Disease and COVID 19 Us...

引用

2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Shilpa, Thoutireddy Kumar, Bura Vijay Yamsani, Nagendar Harshavardhan, A. B V Raju Institute Of Technology Department Of Computer Science And Engineering Medak Telangana Narsapur India School Of Computer Science And Artificial Intelligence Sr University Warangal India Hyderabad500090 India

ISBN: (纸本)9798350371567

The health industry has access to a significant amount of data. All this data is frequently overlooked and underutilized. Many diseases can be diagnosed, prevented, or cured by employing such a large amount of data. Diseases such as cardiovascular diseases, cancer, tumors, Alzheimer's disease, and heart diseases pose a significant threat to humanity, claiming an estimated 17.9 million lives each year. Since its identification in December 2019, COVID-19 has become a pandemic. COVID-19-related deaths are continuing to rise, and early detection is becoming increasingly important. Utilizing ECG copies and deep learning algorithms, an ideal way for automatically diagnosing Corona virus and irregular heartbeats is proposed. ECGs are segmented. Each segment of the ECG is an attribute, and the photos constitute a dataset. Following that, the resulting scanned copies of ECG are given as input into a Convolutional Neural Network (CNN). Using deep learning system and ECG data, it will be possible to identify cardiovascular changes automatically. This along with the appearance of cardiovascular changes due to abnormalities in the heart but also recognizes impact made by COVID-19. This also suggests that the DL model could be used to detect COVID-19 and heart diseases. The proposed study, we hope, might provide a significant decision-making framework for healthcare practitioners. © 2024 IEEE.

关键词： COVID-19

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Walmart Sales Forecasting Using Time Series Analysis

Walmart Sales Forecasting Using Time Series Analysis

引用

2024 international conference on advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Shrieenidhi, A.M. Ruba Roshini, S. Ranjana, P. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The field of retail analytics concentrates on Walmart's challenging issue of forecasting sales by utilizing advanced time series analysis. Walmart is attempting to leverage time series models because it understands how important precise sales projections are for revenue optimization, inventory control, and customer pleasure. By the usage of EDA and Feature engineering, which is capable of capturing underlying trends, temporal patterns, seasonality (Holiday, Weekly Sales, CPI), the impact of external factors (Prominent Holidays) and aiming to improve sales, optimize budgeting and resource allocation, inventory management, business planning is achieved. The principal aim is to provide Walmart a future forecasted sales using traditional time series model such ARIMA & SARIMA. Statistical metrics such as MAE, MSE, RMSE, WMAE have been adopted to evaluate the quality of the model. By refining sales forecasting through advanced time series models and meticulous evaluation, Walmart anticipates not only enhanced operational efficiency but also an elevated customer experience, reinforcing its position as a retail industry leader. © 2024 IEEE.

关键词： Sales

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning 2

Intelligent E-Commerce Chatbot for Personalized Product Reco...

引用

2nd international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Minu, M.S. Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system's scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce. © 2024 IEEE.

关键词： Big Data Chatbot Cost Data Mining e-Commerce Hadoop Machine Learning Product Purchasing Recommendation

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Real-Time Gesture Recognition For Arduino-Based LED And Servometer Manipulation Using OpenCV And Mediapipe 1

Real-Time Gesture Recognition For Arduino-Based LED And Serv...

引用

1st IEEE international conference on Communications and computer science, InCCCS 2024

作者： Kulkarni, Pavan Kumar V. Sudha, M.S. Divya, K.R. Vignesh, S. Sindhu, M. Electronics and Communication Engineering Cambridge Institute Of Technology Bengalore India

ISBN: (纸本)9798350358858

This This project pioneers a real-time gesture recognition system for Arduino platforms using OpenCV and MediaPipe. Analyzing live video streams, the system interprets hand gestures to dynamically control LEDs and servomotors. Bridging the gap between users and technology, it provides an intuitive interface for hardware manipulation. Through computer vision, gesture recognition algorithms, and Arduino programming, the project advances interactive applications in augmented reality and robotics. The abstract encapsulates its essence, emphasizing real-time gesture recognition's focus and its application in seamlessly controlling LEDs and servomotors for Arduino-based systems, assisting with the evolving field of human-computer interaction. © 2024 IEEE.

关键词： Python

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Industrial IoT Systems for Real-Time Environmental Monitoring

Industrial IoT Systems for Real-Time Environmental Monitorin...

引用

2024 international conference on advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Seth, Jyoti Dutta, Abhishek Desai, Karishma Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Innovative technology adoption is crucial for sustainable industrial practices in today's dynamic business climate. EcoSense360 is a revolutionary Industrial Internet of Things (IIoT) system for continuous environmental monitoring in manufacturing facilities, and it is presented in this article. Real-time anomaly detection, predictive maintenance, and energy optimization are the three cornerstone algorithms that make up EcoSense360. As the foundation of EcoSense360, the Real-Time Anomaly Detection Algorithm enables for instantaneous detection of environmental aberrations. The potential for environmental catastrophes is reduced, and crucial concerns may be addressed promptly, thanks to these real-time capabilities. Our testing shows that as compared to conventional approaches, EcoSense360 provides superior outcomes in terms of both data precision and real-time responsiveness, as well as regulatory compliance. EcoSense360 is a proactive and all-encompassing solution for environmental monitoring and management. Real-time environmental insights, predictive maintenance, and sustainable energy use are just a few of the ways in which its algorithms are changing the face of industry. Analysis shows EcoSense360 is superior to conventional approaches to environmental protection, resource conservation, and meeting sustainability targets. EcoSense360 is the best option for businesses that are serious about being environmentally responsible as they face increasing pressure to do so. © 2024 IEEE.

关键词： Environmental monitoring

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Enhancing Face Mask Detection Using Data Augmentation Techniques

Enhancing Face Mask Detection Using Data Augmentation Techni...

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2023 international conference on Recent advances in science and engineering Technology, ICRASET 2023

作者： Kumar, P. Vinodh Kumar, S. Priya, L. Senthilpandi, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350306927

In the ongoing pandemic that we are in general confronting, a cover has turned into a significant extra that we want to wear at whatever point we step out to safeguard ourselves and everyone around us. People who wear masks are to blame for the success of every nation that escaped the pandemic and resumed normalcy. Great reconnaissance estimates that guarantee that the general population is wearing covers consistently can assist us in beating this pandemic. Our existing camera systems can be used by computer vision systems to examine enclosed spaces and recognise individuals without masks. Best-in-class Item Recognition calculations can run at high derivation speeds, which will make this framework basically deployable progressively. Subsequently, further developing the acknowledgment execution of the current face acknowledgment innovation on the veiled faces is exceptionally critical. The latest high-level face acknowledgment approaches are planned in view of profound realisations, which rely upon countless face tests. The training of object detection models that are capable of locating both the unmasked and masked faces in an image is the objective of this paper. These indicators will likewise be hearty to commotion and give as little room as conceivable to oblige bogus up-sides for veils because of the possibly desperate results that they would prompt. © 2023 IEEE.

关键词： Object detection

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