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检索条件"任意字段=2nd International Conference on Algorithms for Big Data, ICABD 2014"

共 223 条记录，以下是91-100 订阅

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IntelliGuard: An AI-Powered Threat Detection System for Smart Home Operations

IntelliGuard: An AI-Powered Threat Detection System for Smar...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Syeda Shakira Akter Md. Maruf Hossain Munna Kazi Redwan Mustakim Ahmed Md. Faruk Abdullah Al Sohan Department of Computer Science American International University- Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The growing application of smart home technologies has exposed significant vulnerabilities in cybersecurity. This research proposes a lightweight, AI-powered Threat Detection System (TDS) aimed at improving the security of IoT devices in smart homes by combining Azure Cloud and Google big-Query for efficient data storage and processing. The proposed system implements firewalls and Transport Layer Security (TLS) encryption to secure communication channels and safeguard critical data from unauthorized access. The TDS utilizes Google bigQuery to analyze extensive IoT data for trained threat detection and mitigation decisions. Some necessary algorithms, such as Deep Q-Networks (DQN) and autoencoders-reinforce its functionality. DQN separates devices exhibiting atypical behavior, and autoencoders identify anomalies in device activity, network traffic, and access patterns. The technology quarantines the damaged device to avoid further damage, using safe data processing in Azure Cloud under strict encryption procedures. The TDS continually analyses data streams in real-time, instantly notifying administrators through a mobile application when abnormalities arise and autonomously upgrading security policies to address evolving threats. This research shows the essential function of cloud-based security solutions and artificial intelligence in safeguarding smart home ecosystems against evolving cyber threats.

关键词： Firewalls (computing) Autoencoders Smart homes Threat assessment Real-time systems Encryption Security Object recognition Internet of Things Artificial intelligence

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A benchmark study of protein folding algorithms on nanobodies

A benchmark study of protein folding algorithms on nanobodie...

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Communications, Computing and Artificial Intelligence (CCCAI), international conference on

作者： Shibo Liang Ziquan Liang Zecheng Wu Feijuan Huang Xu Wang Yuanzhe Cai Bingding Huang Xin Wang College of Big Data and Internet Shenzhen Technology University Shenzhen China. Shenzhen Institute of Translational Medicine Shenzhen 2nd People’s Hospital Shenzhen China College of Communication Engineering Jilin University Changchun Jilin China

Nanobodies, also known as single-domain or VHH antibodies, are recombinant variable domains derived from heavy-chain-only antibodies. They exhibit desirable characteristics, including small size, high solubility, exceptional stability, rapid blood clearance, and deep tissue penetration, rendering them valuable tools for disease diagnosis and treatment. In recent years, several deep-learning-based methods for protein structure prediction have been developed, requiring only protein sequences as input. Notable examples include AlphaFold2, RoseTTAFold, DeepAb, NanoNet, and tFold, which have demonstrated remarkable performance in protein or antibody/nanobody prediction. In this study, we analyzed 60 nanobody samples with known experimental 3D structures from the Protein data Bank (PDB). The accuracy of these algorithms was assessed using two metrics: RMSD and TM-score. Our findings revealed that NanoNet and tFold, particularly NanoNet, exhibit outstanding performance.

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Predicting factors affecting the readiness of big data adoptions: An application of data mining algorithms 2

Predicting factors affecting the readiness of big data adopt...

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2nd international conference on Human-Centered Artificial Intelligence, Computing4Human 2021

作者： Nguyen, Giang Thi Liaw, Shu-Yi Le, Thi Mai Department of Tropical Agriculture and International Cooperation National Pingtung University of Science and Technology Taiwan Department of Business Administration Director of Computer Centre National Pingtung University of Science and Technology Taiwan Department of Social Science Economics and Management International School Vietnam National University Ha Noi Viet Nam

The purpose of this study is to predict the factors affecting the readiness to adopt big data in small and medium enterprises (SMEs) in Vietnam. data collected from 240 managers at SMEs encompasses 13 input variables that impact the readiness (high/low) to adopt big data. Partitioned, training, and testing data were analyzed by three data Mining algorithms techniques (CHAID, Bayesian networks, and Neural Network). The accuracy results of evaluation statistics on the training and testing data of the three models are 70% higher. The area under the Receiver Operating Characteristics (ROC) curve (AUC) value on the training data ranged from 0.827 to 0.908, while it ranged from 0.777 to 0.898 on the testing data. The results of this study highlighted that top management support, data quality, data security, partner pressure, and budget resources are the five most important factors to predict readiness. The findings of this study contribute important implications for managers, service vendors, and policymakers to understand the factors that influence readiness to adopt big data. Hence, managers can establish a clear strategy to enhance the readiness to adopt big data in SMEs in the future. © 2021 CEUR-WS. All rights reserved.

关键词： data mining

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Face Recognition for big data Using Search Engine for Smart System 2nd

Face Recognition for Big Data Using Search Engine for Smart ...

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2nd international conference on Intelligent Systems and data Science, ISDS 2024

作者： Huu, Phat Nguyen Tung, Duong Nguyen Nam, Khanh Nguyen Hoang Minh, Quang Tran Hanoi Viet Nam British International School Ho Chi Minh City Viet Nam 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Linh Trung Ward Thu Duc District Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819796151

In Vietnam, research and design activities related to recognition systems are making initial strides within universities. Previous scientific research and projects predominantly focused on areas like fingerprint recognition to support security systems. However, topics related to human-computer interaction through facial recognition have yet to be fully developed. This paper aims to elucidate various aspects of the SSD model, DeepFace, IrisNet, and FAISS, encompassing their operational principles and IoT applications in security technology through facial recognition for smart door locks. We perform to evaluate several famous models with existing algorithms. The results indicate that the system achieved nearly 93% accuracy for single faces with an execution time of 0.25 s for the Iris model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Face recognition

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2nd international conference on Emerging Technologies in Computing, iCETiC 2019

2nd International Conference on Emerging Technologies in Com...

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2nd international conference on Emerging Technologies in Computing, iCETiC 2019

ISBN: (纸本)9783030239428

The proceedings contain 24 papers. The special focus in this conference is on Emerging Technologies in Computing. The topics include: Smart Airports: Review and Open Research Issues;context-Aware Indoor Environment Monitoring and Plant Prediction Using Wireless Sensor Network;achieving Fairness by Using Dynamic Fragmentation and Buffer Size in Multihop Wireless Networks;a data Science Methodology for Internet-of-Things;a Comparison of the Different Types of Risk Perceived by Users that Are Hindering the Adoption of Mobile Payment;proposing a Service Quality Framework for Mobile Commerce;Sentiment Analysis in E-commerce Using SVM on Roman Urdu Text;prediction and Optimization of Export Opportunities Using Trade data and Portfolio;automatic Speech Recognition in Taxi Call Service Systems;accuracy Comparison of Machine Learning algorithms for Predictive Analytics in Higher Education;a Discussion on Blockchain Software Quality Attribute Design and Tradeoffs;generic Framework of Knowledge-Based Learning: Designing and Deploying of Web Application;the Bearing of Culture upon Intention to Utilize D-learning Amongst Jordanian University Students: Modernizing with Emerging Technologies;tracking, Recognizing, and Estimating Size of Objects Using Adaptive Technique;Analysis Filling Factor Catalogue of Different Wavelength SODISM Images;building Energy Management System Based on Microcontrollers;an Efficient Peer-to-Peer Bitcoin Protocol with Probabilistic Flooding;economic Impact of Resource Optimisation in Cloud Environment Using Different Virtual Machine Allocation Policies;SOSE: Smart Offloading Scheme Using Computing Resources of Nearby Wireless Devices for Edge Computing Services;Securing big data from Eavesdropping Attacks in SCADA/ICS Network data Streams through Impulsive Statistical Fingerprinting.

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big data Analytics: Schizophrenia Prediction on Apache Spark 2nd

Big Data Analytics: Schizophrenia Prediction on Apache Spark

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2nd international conference on Advances in Cyber Security, ACeS 2020

作者： Saputra, Yudhi Fajar Al-Khasawneh, Mahmoud Ahmad Faculty of Computer and Information Technology Al-Madinah International University Kuala Lumpur Malaysia

ISBN: (纸本)9789813368347

Nowadays, the size of the dataset collected from medical record data increases dramatically, ranging from patient demographic data, clinical records, patient symptoms, and nursing diagnoses. No exception to the medical record data on schizophrenia patients, as revealed by WHO that the schizophrenia reaches 20 million in 2019, but the available data can be used for pre-diagnostic tasks regarding schizophrenia cases by adopting the concept of big data analytic. The main objective of this study is to design a prediction model to predict the type of schizophrenia (Paranoid, Catatonic, Residual, Hebephrenic, Symplex, and Undifferentiated) from the medical record dataset of schizophrenic patients. The dataset is then used in a comparative experiment with five machine learning classification algorithms, which are Artificial Neural Network (ANN), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) under the apache spark system on MLlib package. Optimization experiments were also carried out through the L-BGS optimizer for ANN, and 10-folded Cross-validation for four other classification algorithms to obtain optimal results. The best results for the schizophrenia case prediction model were achieved by Random Forest by outperforming five other classification algorithms, with an accuracy of 0.93, a precision of 0.93, a recall of 0.93, and F1-measure of 0.92. This is followed by the performance of ANN, DT, LR, and NB © 2021, Springer Nature Singapore Pte Ltd.

关键词： data Analytics

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Banking Industry Using Artificial Neural Network

Banking Industry Using Artificial Neural Network

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international conference on Futuristic Technologies (INCOFT)

作者： Manoj Hudnurkar Mugdha Kulkarni Symbiosis Centre for Management & Human Resource Development (SCMHRD) Symbiosis International (Deemed University) Pune India Symbiosis Centre for Information Technology (SCIT) Symbiosis International (Deemed University) Pune India

The study aims to create a method to determine the credit rating and evaluate the risks related to the Indian banking business. To create predictive models for credit ratings, Artificial Neural Network (ANN) algorithms are used, which include Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. For this purpose, secondary data from over 254 Indian banks covering 2014–15 to 2021–22 were considered. The study evaluated crucial factors to predict credit scores while considering business, financial, and industrial hazards. The data includes current liability and Revenues, COGS, SG&A, R&D, EBIT, EBITDA, Interest expense, Abnormal Gains/Losses, Income Taxes, Net Income from Discontinued Op., Net Profit, and Dividends. Current and quick ratios. GDP per capita, The research's main contribution is its capacity to forecast how credit ratings for different Indian banks may change over time based on their financial, commercial, and industrial risk profiles. This cutting-edge method of credit rating prediction improves forecasting accuracy. It boosts the effectiveness of rating systems, providing invaluable insights for the banking sector to navigate these uncharted times while analysing credit risks successfully.

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Optimizations of Training dataset on House Price Estimation 2

Optimizations of Training Dataset on House Price Estimation

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2nd international conference on big data Economy and Information Management, BDEIM 2021

作者： Cheri, Xiangqin Aquinas International Academy 5471 Santa Catalina Ave. Garden GroveCA92845-1712 United States

ISBN: (纸本)9781665482882

House price in many cases can strongly reflect local economy, predicting the real estate price can help consumer and the developers. In this way, consumers can inquiry price by giving a series of attributes which describes their ideal house and sellers can get the recommended price by uploading the property description. Thus, a fast, 24-hour responsive auto-inquiry system can greatly improve customers' and sellers' experience. To establish similar system, the dataset which is used to describe the sale of individual residential property in Ames is set as an experiment material. This paper discusses the influence from the data-preprocessing stage on the establishment of later machine learning model using different data groups. Later in the accuracy comparison, controlled experiment is implemented to show the accuracy among different organization of the algorithms. This paper is aimed at how much influence from the model choosing and data preprocessing stage will be made on the prediction accuracy and interpret methods used in preprocessing stage. Furthermore, the experiments show that the single model prediction's accuracy has on average 5% less than the stacked model. In addition, the results represented in this paper have the potential to be integrated into the housing sales system which can be applied to price delivery systems to benefit both consumers and sellers. © 2021 IEEE.

关键词： Machine learning

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

2nd International Conference on Smart Innovations in Communi...

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

ISBN: (纸本)9789811324130

The proceedings contain 47 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Computer Vision-Based Fruit Disease Detection and Classification;Automated Brain Tumor Detection Using Discriminative Clustering Based MRI Segmentation;cancer Prediction Based on Fuzzy Inference System;energy Consumption data Analysis and Operation Evaluation of Green Buildings;the Method of Random Generation of Electronic Patrol Path Based on Artificial Intelligence;Channel Power Estimation for DVBRCS to DVBS2 Onboard DSP Payload;a Slotted Microstrip Patch Antenna for 5G Mobile Phone Applications;reliability Study of Sensor Node Monitoring Unattended Environment;RSOM-Based Clustering and Routing in WSNs;real-Time Meta Learning Approach for Mobile Healthcare;Evaluation of Received Signal Strength Indicator (RSSI) for Relay-Based Communication in WBAN;side-Channel Attacks on Cryptographic Devices and Their Countermeasures—A Review;implementation and Analysis of Different Path Loss Models for Cooperative Communication in a Wireless Sensor Network;Parallel Approach for Sub-graph Isomorphism on Multicore System Using OpenMP;optimized Solution for Employee Transportation Problem Using Linear Programming;spectrum Prediction Using Time Delay Neural Network in Cognitive Radio Network;Reliability Factor Based AODV Protocol: Prevention of Black Hole Attack in MANET;a Survey of Lightweight Cryptographic algorithms for IoT-Based Applications;fruit Disease Detection Using Rule-Based Classification;Fault Tolerance Through Energy Balanced Cluster Formation (EBCF) in WSN;firearm Detection from Surveillance Cameras Using Image Processing and Machine Learning Techniques;Mining Social Networks: Tollywood Reviews for Analyzing UPC by Using big data Framework;cloud-Based E-Learning: Using Cloud Computing Platform for an Effective E-Learning.

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An integrated approach based on two-objective optimization and 2SFCA model for health-care facility location-allocation problems 2

An integrated approach based on two-objective optimization a...

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2021 2nd international conference on Computer Information and big data Applications, CIBDA 2021

作者： Song, Yi Liu, Lu Xia, Lei Harbin Institute of Technology Shenzhen Guangdong518055 China Real Estate Assessment Center Shenzhen Guangdong518000 China

Public health-care facilities are essential to all communities, and their location has long been an important issue in urban planning. With the steady growth of Shenzhen's population, the government needs to decide where health-care facilities should be located to improve the equity of accessibility, and further more to raise the residential needs and satisfaction for the entire population. As a large immigrate city, Shenzhen is a complex socio-ecological system which make it hard to find the best solution that meets all of the objectives. In this article, we establish a multi-objective model to examine the problem of where health-care facilities should be located to improve accessibility for the entire population and decrease the cost of building new facilities. In particular, the model accounts for the different requirements of varying age groups. We used data from Shenzhen and two heuristic algorithms to create the model. The strategy proposed here can be adopted in urban planning and help policymakers reach better decisions. © Published under licence by IOP Publishing Ltd.

关键词： Heuristic algorithms

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