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Serial vs parallel execution of Principal Component Analysis using Singular Value Decomposition 2

Serial vs parallel execution of Principal Component Analysis...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Mallegowda, M. Tanupriya, R. Vishnupriya, C. Kanavalli, Anita Ramaiah Institute of Technology Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306415

Principal component analysis (PCA) is a crucial technique in data science and machine learning for reducing dimensionality and identifying crucial characteristics. But when dealing with big datasets, it uses Singular Value Decomposition (SVD), which is computationally costly. To address this issue, this study compares the effectiveness of parallel and serial PCA implementations. Serial execution uses a step-by-step data processing method, which is simple but falls short when dealing with large datasets. The task is divided across many processing units in parallel execution, which is preferred in high-performance computing environments, and thus results in significant speed increases for large datasets. While parallel execution shines with huge datasets and time-sensitive activities, serial execution improves with smaller datasets and simple scenarios. Depending on variables like dataset size and available computer resources, one of these techniques may be preferred over the other. In conclusion, this paper examines the advantages of utilizing parallel computing for PCA via SVD, offering a quick way to speed up calculations for high-dimensional datasets. It emphasizes how flexible it is to choose serial or parallel execution depending on the particular dataset's properties and processing needs. © 2024 IEEE.

关键词： OpenMP parallel computation Principal Component Analysis(PCA) serial computation Singular Value Decomposition(SVD)

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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Spectral – Vegetative Indices fusion for Crop Yield Analysis 15

Spectral – Vegetative Indices fusion for Crop Yield Analysi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sathvik, Jammula Durga Bala Mohanty, Monish Sushma, N. Raghav, Panchami Amudha, J. John, Maria Dept of Computer Science Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The use of remote sensing in smart farming is gaining popularity around the world. Due to the rising global demand for r food grains, assessing yield before actual production is critical in developing policies and making decisions in the agricultural production system. In this project, the aim is to develop a deep learning model which will predict an individual farm’s crop yield using remotely sensed satellite images. The study focuses on crop yield estimation of wheat, corn and soybean in the chosen study area in KBS-LIER, Michigan, United States. Four vegetative spectral indices, including NDVI and SAVI, etc. were calculated from the seven spectral bands collected over 11 years. Model-I, trained on spectral bands data, achieves an accuracy of 94.3%, outperforming Model-II, which utilizes vegetative indices data and achieves and accuracy of 88.8%]. ©2024 IEEE.

关键词： Crops

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A Review on Cosmetic Product Recommendation Using Deep Learning 4

A Review on Cosmetic Product Recommendation Using Deep Learn...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chouhan, Ruchika Barde, Snehlata Sage University Dept. of Computer Science Engineering Indore India

ISBN: (纸本)9798331515720

Cosmetic products play an important role in every one's life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. The modern lifestyle can be demanding, leaving little time for self-care. However, it's crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual's skin condition. This is because everyone's skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. The main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. This study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. Through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations. © 2024 IEEE.

关键词： Cosmetics

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A Data Safety Approach Based on Image Steganography

A Data Safety Approach Based on Image Steganography

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2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Sushil, Vaibhavi Srivastav, Shashank Dept of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350302820

Presently, the threshold for laptop-based interactions is making it easy for us internationally, to convey data, and also to check financial institution balances, offset payments. Recording securely is an important component that should be the way to ensure communication. Steganography is a method that prevents unauthorized users from accessing important data. With the help of steganography, facts can be hidden in service items including images, text and audio recordings, movies, while performing the function of transmitting data. As a result, this study builds on the many studies found within steganography. It is also used to analyze images that have been done on steganography, and the strengths and weaknesses of each work are explained individually. In this paper, we propose a technique of hiding a data in the image steganography by applying the open CV algorithm along with the Tkinter tool and is implemented in python. We have applied the algorithm for 8-bit pixel of an image which makes the images to display more clearly. © 2023 IEEE.

关键词： Steganography

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Leveraging Name Entity Recognition and Text Classification for Talent Mapping 2

Leveraging Name Entity Recognition and Text Classification f...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Charan, Annepu Sai Moirangthem, Dennis Singh Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

The paper presents a dual-model approach that can be used for categorizing the work experience data from resumes or other textual copra. The application tracking system (ATS), which is the main tool used to screen resumes, presents operational issues in the quickly changing software sector. Although this technique matches resumes with job descriptions effectively, it requires separate scans for every job posting, which could result in errors and inefficiencies. This initiative is motivated by the need to expedite the hiring process for tech companies and potential employees. The suggested approach presents a cutting-edge tool to improve and expedite job matching and resume analysis. The work attempts to improve the software industry's hiring process efficiency by overcoming present system restrictions. This study presents a novel solution - a text classification model intended for resume analysis and shortlisting to address the operational difficulties with the modern resume screening process. Even though the Application Tracking System (ATS) is currently used, it requires separate scans for every job posting, which could lead to inefficiencies. We propose dual-model architecture which includes our custom Name Entity Recognition (NER) Model which is trained on annotated work experience data and a Multi-Layer Perceptron (MLP) model for multi label classification, aiming to streamline the resume screening process. This dual-model approach enhances accuracy and efficiency in resume classification, benefiting both aspirants and tech companies. This research is a pivotal step in optimizing recruitment in the dynamic software industry. The complete work can be found on GitHub1 © 2024 IEEE.

关键词： Mapping

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kumar, Ch Sree Singh, Akhilendra Pratap Reddy, K Hemant K National Institute of Technology Meghalaya Dept. of Computer Science Meghalaya India VIT-AP Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350369175

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes. © 2024 IEEE.

关键词： Distributed ledger

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Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches 3

Advancements, Methods, and Obstacles in Emotion Recognition ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arunkumar, M. Athiban, R. Selva Jothi, M. Pavithra, S. Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331527549

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field. © 2025 IEEE.

关键词： Contrastive Learning

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Optimizing Cloud Security in Dynamic Environments Through Adaptive Workload Management Algorithm

Optimizing Cloud Security in Dynamic Environments Through Ad...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Yadav, Monika Sharma, Garima The Northcap University Dept. of Computer Science and Engineering Haryana India

ISBN: (纸本)9798350375237

With the growing interest of researchers in the area of cloud computing, security emerges as a major area of concern. As the complexity of cloud environments grows and the dependency on cloud services deepens across various sectors, ensuring robust security measures is crucial. This study proposes an innovative approach to enhance cloud security through efficient workload scheduling strategies. Leveraging insights from existing studies on green cloud computing, clonal selection algorithms, and energy-efficient resource allocation, the research introduces a novel security-aware workload organizing algorithm. The proposed algorithm aims to optimize both energy consumption and security in cloud data centers. Through comprehensive simulations on the Cloud Sim platform, the results demonstrate the algorithm's efficiency in reducing energy consumption while fortifying security measures. Moreover, the present study contributes to the evolving field of cloud security, offering a promising avenue for shielding data in dynamic cloud environments. © 2024 IEEE.

关键词： Energy utilization

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