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检索条件"机构=Artificial Intelligence and Data Science Faculty of Engineering And Technology"

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A System for Real-Time Shoulder Angle Measurement Using Computer Vision

A System for Real-Time Shoulder Angle Measurement Using Comp...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Meher Langote Saniya Saratkar Chetan Puri Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Higher Education and Research Wardha Maharashtra India Department of Computer Science Design Faculty of Engineering and Technology Datta Meghe Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Recently, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. This article presents a real-time shoulder angle measurement system utilizing computer vision and machine learning techniques, specifically designed for the rehabilitation of frozen shoulder and related conditions. The system leverages MediaPipe, an open-source framework, to capture and analyze shoulder movements accurately. With its modular architecture, the system processes video feeds in real time, detecting key body points and calculating shoulder angles, thereby providing immediate feedback to patients and therapists. The study evaluates the system's effectiveness in clinical and home-based environments, highlighting its potential to enhance physiotherapy by enabling precise, data-driven treatment plans. This research study also identifies current research gaps, proposes solutions, and explores future enhancements, including environmental adaptation, mobile integration, and the extension to other joints. This study has attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI), Computer vision, and a machine learning mode.

关键词： Computer vision Machine learning algorithms Shoulder Medical treatment Machine learning Streaming media Ubiquitous computing Real-time systems Motion measurement Smart phones

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Blockchain-Based Identity Management: Enhancing Privacy and Security in Digital Identity System 7

Blockchain-Based Identity Management: Enhancing Privacy and ...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Saranya, S. Monika Manikandan, K. Nagaraju, Jangam Nagendiran, S. Geetha, B.T. Dr. N.G.P. Institute of Technology Computer Science and Engineering Coimbatore India Mangalmay Institute of Management and Technology Greater Noida India Tamilnadu Vellore632014 India Mlr Institute of Technology Department of Data Science Dundigal Hyderabad India R. M. K. Engineering College Department of Artificial Intelligence and Data Science India Department of Ece Tamil Nadu India

ISBN: (纸本)9798350350067

Privacy and security are major concerns when it comes to digital identities, particularly in centralized systems where data can be easily hacked or where people engage in identity fraud. These issues can be addressed using blockchain technology since it allows for the creation of a decentralized digital identity that is both secure and tamper-proof. This study employs Kaggle dataset to document the transactions and the devices involved to make the distinctions on how blockchain can boost privacy and security. Predominantly, various machine learning algorithms are used to analyze blockchain applications, and the results emphasize the fact blockchain systems greatly enhance transaction viability and diminish fraudulent actions. Therefore, the study argues that blockchain-based identity management systems offset the shortcomings of the centralized model by enhancing data ownership and combating fraud. Subsequent analysis expanded on the discussion of elaborating the scalability, interoperability, and application of algorithms to blockchains as well as how it can fundamentally change the way that certain industries, particularly identity management, would work with consumers at the center of reliable and secure blockchain solutions. © 2024 IEEE.

关键词： Differential privacy

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model 4

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Saranya, V.S. Subbarao, Garimidi Balakotaiah, Dega Bhavsingh, Maloth Babu, K. Suresh Dhanikonda, Srinivasa Rao School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai603 203 India India Department of Computer Science and Engineering India Ashok: Womens Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Department of Computer Science and Engineering A.P Narasaraopet India Department of Artificial Intelligence and Data Science Telangana Hyderabad India

ISBN: (纸本)9798331540364

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.

关键词： Convolutional neural networks

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DigiFarmer: Precision Agriculture Empowered by Machine Learning for Sustainable Crop Management

DigiFarmer: Precision Agriculture Empowered by Machine Learn...

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Networking and Communications (ICNWC), International Conference on

作者： Karan Devanand Patra Arya Adesh Bhosle Deepali Mukesh Kothari Tatwadarshi P. Nagarhalli Dept. Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In agriculture, the sector grapples with pressing challenges arising from climate change, water shortages, and pest infestations. Particularly affecting farmers in developing nations, these challenges amplify issues of food insecurity and poverty, necessitating innovative solutions for sustainable global agricultural progress. Addressing the complexities of climate variability, water scarcity, and pest outbreaks requires comprehensive tools and information. The absence of predictive insights intensifies crop losses and hampers overall profitability. In response, this research introduces DigiFarmer, an innovative solution employing advanced machine learning models. DigiFarmer adopts a holistic approach, encompassing crop disease detection, weed classification using Convolutional Neural Networks (CNNs), and crop quality analysis with a Light Gradient Boosting Machine (LGBM) classifier. Yield prediction is facilitated by the Random Forest algorithm, with real-time weather data integration enhancing efficacy. DigiFarmer stands out for empowering farmers with tools for sustainable and resilient agriculture. Leveraging cutting-edge technologies, DigiFarmer not only addresses immediate challenges but equips farmers with foresight to mitigate future risks. The ultimate goal is to contribute to the welfare of global farming communities by enabling informed decisions that enhance productivity, profitability, and sustainability in agriculture.

关键词： Productivity Adaptation models Machine learning algorithms Profitability Crops Boosting Prediction algorithms

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine 10

Predicting Hand Injury Severity Using Bayesian Networks in C...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Yuvaraj, S. Thinakaran, K. Srinivas, S. Porandla Sivakumar, S. Ishwarya, M.V. Thirumaraiselvan, P. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Chennai Tamil Nadu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Department of Computer Science Tamil Nadu Perambalur India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model's training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes. © 2024 IEEE.

关键词： Bayesian networks

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Decoding the News: An Integrated Approach for News Text Classification and Expert System Recommendations

Decoding the News: An Integrated Approach for News Text Clas...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Vivek Prajapati Arpit Mishra Prathmesh Shinde Neha Raut Dept. of Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

In the era of information overload on online platforms, efficient access to news content has become crucial for individuals leading busy lives. This paper addresses the need for categorizing vast amounts of online news data into specific sections like sports, entertainment, business, finance, etc., catering to users’ personalized interests. Leveraging Machine Learning and Deep Learning algorithms, the study utilized a BBC News dataset from Kaggle and supplemented it with an additional dataset generated through web scraping using BeautifulSoup. With up to 20,000 rows covering sports, business, entertainment, politics, and technology categories, the research applied Random Forest, Support Vector Machine (SVM), Gaussian Naive Bayes (NB), and Multi-Head Attention Transformer algorithms. The findings and recommendations extend to technologies suitable for both news text classification and user-specific content recommendation in this evolving digital landscape.

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A Few-Shot Learning-Based Domain-Adaptive Lightweight CNN for Robust Hand Gesture Recognition in Real-Time Applications 2

A Few-Shot Learning-Based Domain-Adaptive Lightweight CNN fo...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Shobana, K.B. Kaladeep Yalagi, Pratibha C. Ammu, V. Aldo Stalin, J.L. Kumar, Prashant Richard, Titus Department of Artificial Intelligence and Data Science Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Walchand Institute of Technology Solapur India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Information Technology Sona College of Technology Salem India Department of Information Technology Project Circle BSNL Lalkuthi Kolkata India Department of English Vels Institute of Science Technology & Advance Studies Pallavaram Chennai India

ISBN: (纸本)9798331504960

Surface electromyogram-based hand gesture recognition (sEMG) has promising prospects in human-machine interaction applications, including the neural rehabilitation and assistive technologies. However, this technique also has several disadvantages, such as poor generalization across subjects and days, demanding a large amount of data for calibration, and computational complexity that does not enable the real-time deployment of the system. The proposed work incorporates a novel few-shot learning-based domain-adaptive lightweight CNN that is specifically designed to be used in robust and efficient hand gesture recognition. This minimizes the dependency on the labeled data of other new subjects or days, hence allowing better adaptability in real-world scenarios. The proposed lightweight CNN architecture adapts well to wearable devices with real-time performance but high accuracy. Another would be adding domain-adaptive feature learning to account for variations between inter-subject and inter-day domains while preserving the accuracy of classification and improving model generalization. The proposed framework is able to achieve competitive accuracy using a small calibration set in gesture-based human-machine interfaces continuously and broadly. Results on high-density sEMG datasets have shown that our model achieves a good balance between the accuracy (95.5%) and efficiency on experimental results and holds promise for providing a reliable solution for real-time cross-domain hand gesture recognition applications. © 2024 IEEE.

关键词： Calibration

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Optimization of Agriculture Using data science and Machine Learning

Optimization of Agriculture Using Data Science and Machine L...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： P.Josephin Shermila Syed Atheequr Rahaman Nithosh Balaji.R Sai Manoj Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The research aims to optimize agriculture using data science techniques. Agriculture is a critical sector for sustaining life on earth, and optimizing it can enhance food security and increase the profitability of farmers. data science techniques such as machine learning, data analysis, and modeling to analyze agricultural data and derive insights are the key areas in this work. The project will focus on enhancing crop productivity, reducing wastage, and improving resource utilization. The findings of the project can help policymakers, farmers, and stakeholders to make informed decisions and take appropriate actions. The project aims to support the sustainable agriculture practices and promote the efficient use of resources for the benefit of both farmers and consumers.

关键词： Temperature measurement Productivity Temperature Crops Machine learning Soil data science

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Graph-based Named Entity Information Retrieval from News Articles using Neo4j

Graph-based Named Entity Information Retrieval from News Art...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Shikha Chaudhary Hirenkumar Vyas Naveen Arora Sejal D’Mello Dept. of Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

The internet, social media, and other cutting-edge technologies are all contributing to the exponential growth of digital information. This makes interpreting and applying this enormous amount of data both possible and challenging. Our research proposes a smart system that can convert unprocessed text into a knowledge graph in order to address this. This system makes use of the graph database Neo4j. Although there are currently available tools for linking entities and models to extract relationships, such as Spacy, NLTK, and Flair, their combined use is inefficient. Our suggested method seeks to efficiently blend entity linkage and relation extraction, allowing us to convert raw data into a knowledge graph. There are many real-world uses for this strategy, particularly in data analysis and decision-making.

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