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Prediction of Heart Disease Using ML Algorithms 1

Prediction of Heart Disease Using ML Algorithms

引用

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

作者： Panda, Ashok Kumar Pati, Chinmayee Pradhan, Sonali Pradhan, Amrutanshu Rath, Naba Kumar Silicon University Department Of Computer Science & Engineering Bhubaneswar India School of Computer Sciences Odisha University of Technology and Research Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798331518578

There are a variety of approaches utilised in the processing of healthcare data. Data mining is utilised quite frequently. The majority of deaths are caused by cardiovascular disorders. It is a risk factor for heart disease. The proportions of risk factors for heart disease can be computed using this technique. Data sources can be categorised based on healthcare factors. Using a data mining classification approach, this system does analysis on such parameters. The datasets are analysed using the Decision Tree Algorithm and the Naive Bayes Algorithm, both of which are written in Python. The Decision Tree Algorithm has the highest precision for heart disease. © 2025 IEEE.

关键词： classification techniques Data mining Heart disease Machine Learning methods

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Dynamic feature elimination method based on target detection and depth information 5

Dynamic feature elimination method based on target detection...

引用

5th international Academic Exchange conference on science and technology Innovation, IAECST 2023

作者： Li, Xunjia Wang, Zhiming Shanghai University School of Mechanical and Electrical Engineering and Automation Shanghai China

ISBN: (纸本)9798350357738

SLAM (simultaneous localization and mapping) technology is a key technology in the field of robotics. Visual SLAM has received widespread attention because of its low cost and rich information. However, traditional SLAM is based on the assumption of static environment. This paper proposes a method to eliminate dynamic feature points in SLAM systems. First, a target detection algorithm is used to obtain potential dynamic points, and then depth information is used to find and eliminate dynamic feature points. Use geometric relationships to check motion consistency, find dynamic frames that are not defined in advance, and use depth information to eliminate dynamic feature points. Only remaining static features are retained in the tracking and optimization module to achieve accurate camera tracking in dynamic environments. Pose estimation. This improves the stability and accuracy of SLAM systems in dynamic environments. © 2023 IEEE.

关键词： robotics

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An Integrated Development Of IoT Based Machine Learning For Data-Driven Travel Recommendations

An Integrated Development Of IoT Based Machine Learning For ...

引用

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

作者： Rifath, Shakeel Sridhar Xavier, Constance Brindasri, S. Hindustan Institute Of Technology And Science Computer Science And Engineering Department Chennai India

ISBN: (纸本)9798350364828

An innovative methodology is introduced in this Research to enhance data-driven travel decision-making by integrating IoT technology and data science techniques. With a specific focus on air and sound pollution, the research utilizes real-time sensor data from various urban sources to develop predictive models for forecasting future pollution levels. These models serve as the backbone for decision support tools seamlessly integrated into open-source platforms like ThingSpeak and IFTTT, facilitating proactive travel planning for both the general public and patients. The core of this methodology lies in the real-time aggregation of IoT sensor data, offering users comprehensive insights into prevailing air pollution levels at their intended destinations. Augmented by predictive machine learning algorithms such as logistic regression and decision trees, the system's real-time monitoring capabilities are significantly enhanced, with the decision tree algorithm particularly noted for its accuracy in assessing destination safety for patients. The forecasting accuracy and reliability of the model are upheld through rigorous training on historical data. Notably, the synergy between ThingSpeak and IFTTT enables the delivery of personalized travel recommendations grounded in predictive analytics and real-time environmental monitoring. This research underscores the pivotal role of data-driven decision-making in travel planning, empowering patients with tailored guidance derived from robust data analysis. © 2024 IEEE.

关键词： Predictive analytics

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Secured Homomorphic Encryption and Authentication for Healthcare

Secured Homomorphic Encryption and Authentication for Health...

引用

2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Sharma, Swapnil Sarmah, Uditaparna Dodamani, Vineet M. Vikas Rai, K. Ramachandra, H.N. Shivaprakasha, K.S. N.M.A.M. Institute of Technology Department of Ece Nitte574110 India

ISBN: (纸本)9798350388602

Secure communication and data processing is one of the vital requirements for applications like healthcare, feedback systems, voting etc. In the traditional cryptographic algorithms, the data is to be decrypted before processing. When the processing is carried at a third party, the privacy is compromised. Thus, such applications require a system where the data processing is done without actually knowing the data. Homomorphic Encryption (HE) is one such solution to this problem where, the encrypted data is processed at the third-party level without performing decryption. It provides secured data communication between sender(s) and receiver by preserving privacy. It not only assures privacy but also avoids multiple decryption operations to perform data processing. Nevertheless, one of the challenges in this system is to guarantee the authenticity of the data requester. This can be accomplished using authentication protocols over HE. This paper proposes an Authentication Protocol over a Partial Homomorphic Encryption (PHE) system for healthcare data. The patient data is processed at cloud in encryption domain and the results are sent to the data requester on proving one's authenticity. This ensures both privacy of the information and authenticity of the data requester. © 2024 IEEE.

关键词： Differential privacy

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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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Blockchain: A secure solution for identifying counterfeits and improving supply chain reliability

Blockchain: A secure solution for identifying counterfeits a...

引用

2024 international conference on advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Farhan, Maruf Sulaiman, Rejwan Bin Nur, Abdullah Hafez Northumbria University Department of Computer Science and Engineering Northumbria United Kingdom Northumbria University Department of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350350289

Counterfeit goods provide a substantial risk to both consumers and corporations. This study presents a system that employs blockchain technology to address the issue of counterfeiting by validating the authenticity of products using QR codes. The framework enables manufacturers to register products and monitor their ownership across the whole supply chain. Subsequently, consumers have the ability to scan QR codes of products in order to authenticate their authenticity and obtain information regarding the product's past. The suggested system has numerous benefits, such as: Enhanced security: Blockchain technology guarantees the integrity of data storage and transaction records, making them resistant to tampering. Enhanced transparency: Consumers are able to obtain comprehensive information regarding the product's trajectory, promoting trust and assurance. Cost reduction: The solution obviates the necessity for intermediaries and manual verification procedures, resulting in financial savings. The study also recognises constraints such as the requirement for specialised knowledge and possible difficulties in expanding the solution. Furthermore, the efficacy of the system hinges on the extensive acceptance and understanding of blockchain technology. In summary, this article introduces a potential framework for utilising blockchain technology to tackle the increasing problem of counterfeit items. Subsequent investigations should prioritise enhancing the system's scalability, security, and user acceptance to optimise its overall impact © 2024 IEEE.

关键词： Smart contract

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Real-Time Convolutional Neural Networks for Emotion and Gender Classification 2

Real-Time Convolutional Neural Networks for Emotion and Gend...

引用

2nd international conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Singh, Jagendra Singh, Akansha Singh, Krishna Kant Lal, Bechoo Verma, Harsh Samudre, Niranjan Raperia, Harsh School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Delhi Technical Campus Greater Noida201310 India Department of Computer Science and Engineering Konenru Lakshmaiah Education Foundation vaddeswaram India Department of Electronics and Computer Science Atharva College of Engineering Mumbai India

Emotion and gender recognition are important areas of research in the field of computer vision and human-computer interaction. The proposed CNN architecture is designed to extract features from facial images and classify them into six basic emotions (happy, sorrow, anger, fear, surprise, and disgust) and two genders (male and female) in real-time. To extract and categorize characteristics from facial photographs, the suggested CNN architecture consists of convolutional layers, pooling layers, and fully connected layers. The suggested system performs at the cutting edge for both emotion and gender recognition tasks when tested on publicly accessible datasets. The proposed real- time CNN architecture has potential applications in various fields, including social robotics, human-computer interaction, and affective computing. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolution

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AI algorithms for Autonomous robotics Navigation 2

AI algorithms for Autonomous Robotics Navigation

引用

2nd international conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Komala, C.R. Raja, J. Marotrao, Sarange Shreepad Neelima, Nalla Sujatha, R. Chidambarathanu, K. East Point College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru560049 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering School of Computing Tamil Nadu Chennai600062 India Ajeenkya D Y Patil School of Engineering Department of Mechanical Engineering Maharashtra Pune412105 India R.V.R & J.C. College of Engineering Department of Information Technology Andhra Pradesh Chowdavaram522019 India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai625022 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai601206 India

ISBN: (纸本)9798350364699

Robotic systems depend much on autonomous navigation since it allows them to operate on their own in environments constantly changing and lacking clarity. The most evolved artificial intelligence (AI) techniques applied in self-driving robot navigation are briefly reviewed in this work. These approaches call for classical methodologies, machine learning, and deep learning as well. We examine the advantages and drawbacks of numerous approaches applied in route planning, location finding, map creation, obstacle avoidance. We investigate how neural networks, sensor fusion, and reinforcement learning may be applied to provide increased dependability and flexibility for guiding systems. Case examples illustrating the advantages and drawbacks of applying artificial intelligence (AI) in practical contexts including service robots, drones, and self-driving cars abound. Combining modern AI algorithms with conventional approaches indicates that it is feasible to create self-driving robots more dependable and efficient based on the increased tracking performance. © 2024 IEEE.

关键词： Reinforcement learning

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Fixed-time sliding mode extended state observer-based tracking control for cable-driven parallel robots with uncertain dynamics 5

Fixed-time sliding mode extended state observer-based tracki...

引用

5th international conference on Control, robotics, and Intelligent System, CCRIS 2024

作者： Lu, Yanqi Xu, Ruiqi Yao, Weiran School of Astronautics Harbin Institute of Technology Harbin China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

The development and applications of cable-driven parallel robots (CDPRs) are restricted due to model uncertainties and external disturbances, which decrease the positioning accuracy of CDPRs. To address this issue, an imitated active disturbance rejection controller (IADRC) is proposed in this paper. A fixed-time sliding mode extended state observer (FTSMESO) is designed to estimate the lumped uncertainties via the switching sliding mode variable. The fixed time convergence of estimation errors is guaranteed, and the convergence time is unrelated to the system’s initial state. Based on the FTSMESO, an IADRC is proposed and its robustness is analyzed. The simulations and experiments are conducted, and the results indicate that the designed FTSMESO effectively observes the lumped uncertainties and verifies the practicality and effectiveness of the IADRC. © 2024 SPIE.

关键词： Disturbance rejection

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Optimal Energy Management for Residential with Renewable Energy Integration 1

Optimal Energy Management for Residential with Renewable Ene...

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1st international conference on robotics, engineering, science, and technology, RESTCON 2024

作者： Tiv, Dararith Sorn, Darong Heav, Tenghor Khon, Kimsrornn Institute of Technology of Cambodia Faculty of Electrical and Energy Engineering Phnom Penh Cambodia Institute of Technology of Cambodia Research and Innovation Center Phnom Penh Cambodia

ISBN: (纸本)9798350348842

The exponential surge of residential renewable energy integration presents a burgeoning challenge: balancing real-Time energy optimization with the delicate interplay between economic viability and environmental responsibility. This paper confronts this critical frontier by proposing a novel Mixed Integer Linear Programming (MILP)-based Energy Management System (EMS) algorithm, meticulously designed to optimize cost-efficient energy consumption within residential buildings. Our groundbreaking algorithm seamlessly integrates battery storage for enhanced operational flexibility and effortlessly connects to existing grid infrastructure through established protocols. Comprehensive case studies, crafted using historical data from a representative sample of residential dwellings, serve as robust testing grounds for our approach. Compared to traditional methods, our algorithm delivers a demonstrably superior performance, facilitating substantial energy cost reductions. This paves the way for a future of affordable and environmentally conscious energy solutions in residential communities. The system's seamless integration positions it as a key driver for sustainable energy policies within the residential sector, potentially fostering peak demand reduction and enhancing grid stability. Our novel MILP-based EMS algorithm illuminates a path toward a cleaner, decentralized, and cost-effective future powered by residential renewables. By optimizing energy consumption, facilitating substantial cost savings, and effortlessly integrating with existing infrastructure, it provides a critical foundation for sustainable energy policies and paves the way for a future where clean energy thrives within the very heart of our communities. © 2024 IEEE.

关键词： Energy management systems

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