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Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm

SN computer science 2024年第6期5卷 734页

作者： Kari, Venkatram Amalanathan, Geetha Mary School of Computer Science and Engineering Vellore Institute of Technology University

Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machines struggle due to variations in expression. This challenge underscores the necessity for a robust framework capable of matching entities based on their attributes. While previous studies have explored transformation techniques, NLP, and deep learning, they often fall short in accuracy and scalability or overlook internal constraints. Such shortcomings result in costly rework and compromised business decisions. To address these issues, proposed “Deep Semantic Homophonic Synonymy” (DSHS) algorithm, a hybrid approach integrating rule-based, NLP, and deep learning techniques. By employing semantic matching, rule-based preprocessing, NLP enrichment, phonetic analysis, abbreviation variations, and spelling corrections, the DSHS algorithm effectively overcomes entity matching challenges. This solution, comprising six modules including Preprocessing, Transformation, Translation, Word Embedding, Attribute Similarity, and Classification, outperforms existing EM models in terms of accuracy and scalability. This solution is a comprehensive entity matching, offering significant advancements in the field and providing a reliable framework for resolving complex matching requirements. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data pipeline Data sync up MDDR Message queue Schema registry

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Real-Time Elderly Fall Detection as Ambient Intelligence in Old Age Homes 3

Real-Time Elderly Fall Detection as Ambient Intelligence in ...

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

作者： Shabadi, Laxmi Chaitra, M. Monik, L. Pavan, S.G. Deekshith, R.K. Narayan, Laxmi Sjb Institute of Technology Department of Computer Science and Engineering Bangalore India Bnm Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly because falls from this age group often result in fatalities and serious injuries. Fall detection devices that can quickly recognize falls and alert emergency services have become more and more popular as a result. The primary goal of the project is to increase elderly home safety by implementing an ambient intelligence-based automated emergency recognition system. We present a unique method for fall posture detection utilizing an intelligence surveillance camera and a class of efficient models called MobileNets for mobile and embedded vision applications. A real-time embedded solution persuaded from 'edge AI' is implemented using the state-of-the-art Object detection: single shot detector (SSD) Mobile Net V2 and Internet of things embedded GPU platform NVIDIA's Jetson Nano. © 2025 IEEE.

关键词： Ambient intelligence

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Comparing HAProxy Scheduling Algorithms during the DDoS Attacks

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 139-142页

作者： Kumar, Anmol Somani, Gaurav Agarwal, Mayank Indian Institute of Technology Patna Department of Computer Science and Engineering Patna800013 India Central University of Rajasthan Department of Computer Science and Engineering Ajmer305817 India

Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financial and reputation losses. Load balancing software such as HAProxy (high availability proxy) is an important building block of the Web service delivery today. Load balancing software provides incoming request distribution among Web-servers and admission control which may even help with combating DDoS attacks. In this letter, we study and explore the performance of different load distribution or scheduling strategies in the presence of DDoS attacks. With the help of attack experiments, we find that First, HDR, Source, and URI scheduling algorithms performs best for both static and Poisson arrivals. © 2019 IEEE.

关键词： Scheduling algorithms

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An Effective Analysis and Exploration of Cutting-Edge Machine Learning for Protein Structure and Sequence Prediction

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SN computer science 2024年第6期5卷 1-12页

作者： Alam, Afaque Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna

When processing large amounts of data, proteins play a key role in biological processes. Protein structure prediction relies on a procedure called relevant feature selection. To accomplish classification, a feature selection technique must first identify the most important features inside the massive dataset. The proposed methods improve protein structure prediction by providing data from the protein identification and sequencing process. Here we present a novel method for predicting protein structure using PSI sequencing. Gibbs Entropy Simulated Forging is used to pick important properties from a protein dataset. Gibbs Entropy is used to quickly locate the important amino acid properties from the massive dataset. The big data protein is sequenced using the Edman Firefly Optimization procedure after the appropriate features have been identified. A firefly's light intensity is calculated with the use of an objective function. Selected amino acid characteristics are ordered by light intensity rating. As a result, it is put to use in the fast and precise formation of protein sequences. This results in improved accuracy when sequencing proteins and a shorter sequencing time when determining each peptide's amino acid sequence. Accuracy and recall in the GESF algorithm procedure have increased while protein sequencing has required less time. The protein sequence is then constructed by applying Edman Firefly Optimization to a subset of characteristics. To improve protein correctness with little efforts investment, peptide residues are sequenced using EFO. Several large protein datasets are used to conduct an experimental evaluation in terms of precision, recall, false positive rate (FPR), sequencing accuracy (SQR), and sequencing time. The findings demonstrate that GESF-EFO improves upon existing methods of protein sequencing in the context of massive datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accuracy EFO GESF SA ST

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Hermes - Smart College Application using Android 2

Hermes - Smart College Application using Android

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Dimlo, U.M. Fernandes SilpaRaj, M. Srilakshmi, Ch. Kamesh, D.B.K. Balaram, Allam Sreyas Institute of Engineering and Technology Department of Cse India India Vnr Vignana Jyothi Institute of Enginnering and Technology Department of It Hyderabad India Malla Reddy Engineering College for Women Department of Cse India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350370188

Android apps emerged with modern technology. It's time to ditch websites for apps. We are thrilled to debut the Smart-College app, which will act like a condensed college website. The teaching staff and parents of students in their classrooms will use this app to track attendance, grades, and campus club activity. This tool helps colleges, students, and parents communicate more closely. Logging in with their credentials lets parents track their child's progress via the app. Teachers can record attendance using the programme, and if a student is absent, parents' mobile devices will be notified promptly. This project aims to reduce parents' academic responsibilities. This app was developed in Android Studios and deployed on AWS Server. © 2024 IEEE.

关键词： Studios

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Function Scheduling with Data Security in Serverless Computing Systems 17

Function Scheduling with Data Security in Serverless Computi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Saha, Srestha Pandey, Arvind Addya, Sourav Kanti Brata Nath, Shubha Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331531195

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.

关键词： Simulated annealing

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A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model 2

A Hybrid IoT and Machine Learning Approach for Crop Recommen...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, A. Geetha Vani, B. Lavanya, M. Kumar, R.G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering A.P India

ISBN: (纸本)9798350317558

Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the system depends on it to some extent, improvements are still required. This study presents an Internet of Things (IoT) based crop recommendation system that uses Machine learning (ML) to use the value that IoT devices collect from the soil. Initially, we utilized baseline ML classifier models like Support vector machine (SVM), K-Nearest Neighbor(KNN), Random forest (RF), and Random forest (NB).Then we employed an ensemble model to detect the suitable crops. We employed several IoT sensors in our research. The DHT11 sensor collects real-time environmental data, such as temperature and humidity. During crop recording, vital soil parameters like potassium and phosphorus - which are essential for crop growth - are measured using an NPK sensor, a pH sensor, and a rain sensor. These sensors can be seamlessly integrated by the IoT device, offering an effective way to collect crucial agricultural data. The Voting Base Ensemble performs exceptionally well in terms of precision, recall, and F1 score measures. The voting Base Ensemble algorithm improves metrics such as F-score, recall, and precision by up to 98%. A thorough evaluation of soil conditions is made possible by the integration of various sensors, and the application of an ensemble model enables accurate crop recommendations based on environmental factors. © 2024 IEEE.

关键词： Agriculture Crop Recommendation Ensemble IoT Machine Learning

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Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

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International Journal of computers and Applications 2024年第12期46卷 1152-1165页

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Emotion Recognition

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Leveraging Network Slicing in SDNs for Handling Application Failures

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

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Soundtrack Success: Unveiling Song Popularity Patterns Using Machine Learning Implementation

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SN computer science 2024年第3期5卷 278页

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The emotional reactions of the music listeners may cause physiological changes as well as neurological benefits. However, any musician should avoid being influenced by an algorithmic recommendation presented in the research. The most prominent aspect for any musician is to stay true to their own musical style and to be novel, honest, and sincere. The main objective of this research work is to provide precise music predictions to the artists before they launch their music in the market. This work also aims to improve the accuracy of the prediction models using some feature-engineering-based techniques to manipulate the Spotify dataset and predict whether the song will be a hit or not. The proposed work leverages state-of-the-art machine learning models, including Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest, GBM, and neural networks to explore the predictive factors influencing the inclusion of a particular music track in the featured Spotify Hit-50 list. Furthermore, it offers descriptive statistical analysis of the features that can help in popularity prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Hit song science audio features Music information retrieval Music streaming data Regression

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