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检索条件"任意字段=3rd International Conference on Advances in Computing and Data Sciences, ICACDS 2019"

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3rd international conference on advances in computing and data sciences, icacds 2019

3rd International Conference on Advances in Computing and Da...

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3rd international conference on advances in computing and data sciences, icacds 2019

ISBN: (纸本)9789811399411

The proceedings contain 112 papers. The special focus in this conference is on advances in computing and data sciences. The topics include: Lexical Text Simplification Using WordNet;analysis and Impact of Trust and Recommendation in Social Network Based Algorithm for Delay Tolerant Network;formation of Hierarchies in the System of Organization of State Construction Supervision in Case of Reorientation of Urban Areas;customized Visualization of Email Using Sentimental and Impact Analysis in R;Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs;Five Input Multilayer Full Adder by QCA Designer;effect of Vaccination in the Computer Network for Distributed Attacks – A Dynamic Model;ransomware Analysis Using Reverse Engineering;semantic Textual Similarity and Factorization Machine Model for Retrieval of Question-Answering;empirical Study of Test Driven Development with Scrum;krisha: An Interactive Mobile Application for Autism Children;advanced Spatial Reutilization for Finding Ideal Path in Wireless Sensor Networks;an Efficient Preprocessing Step for Retinal Vessel Segmentation via Optic Nerve Head Exclusion;An Upper Bound for Sorting Rn with LE;image Filtering with Iterative Wavelet Transform Based Compression;Multiresolution Satellite Fusion Method for INSAT Images;a Combinatorial Fair Economical Double Auction Resource Allocation Model;issues in Training a Convolutional Neural Network Model for Image Classification;an Approach to Find Proper Execution Parameters of n-Gram Encoding Method Based on Protein Sequence Classification;hyperglycemia Prediction Using Machine Learning: A Probabilistic Approach;microprocessor Based Edge computing for an Internet of Things (IoT) Enabled Distributed Motion Control;rough-Set Based Hotspot Detection in Spatial data.

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3rd international conference on advances in computing and data sciences, icacds 2019

3rd International Conference on Advances in Computing and Da...

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3rd international conference on advances in computing and data sciences, icacds 2019

ISBN: (纸本)9789811399381

The proceedings contain 112 papers. The special focus in this conference is on advances in computing and data sciences. The topics include: A deep learning approach to speech recognition of digits;an architecture for analysis of mobile botnet detection using machine learning;thyroid prediction using machine learning techniques;an iterative scheme for a class of fractional order perturbed differential equations;a novel algorithm to compute stable groups in signed social networks;deep convolution neural network based denoiser for mammographic images;detection of brain tumor using machine learning approach;fuzzy petri net representation of fuzzy production propositions of a rule based system;Fraud detection in medical insurance claim with privacy preserving data publishing in TLS-n using blockchain;business forecasting in the light of statistical approaches and machine learning classifiers;MR brain image tumor classification via kernel SVM with different preprocessing techniques;Attribute-based deterministic access control mechanism (AB-DACM) for securing communication in internet of smart health care things;an enhanced genetic virtual machine load balancing algorithm for data center;a study of deep learning methods for mitotic cell detection towards breast cancer diagnosis;Hybrid approaches for brain tumor detection in MR images;SeLF: A deep neural network based multimodal sequential late fusion approach for human emotion recognition;animal-vehicle collision mitigation using deep learning in driver assistance systems;classification of parkinson’s disease using various machine learning techniques;detection of malignant melanoma using deep learning;SARPS: Sentiment analysis of review(s) posted on social network;Impact of feature extraction techniques on a CBIR system.

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Application of Machine Learning Algorithms in Predicting the Heart Disease in Patients 3

Application of Machine Learning Algorithms in Predicting the...

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3rd international conference on advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Razia Sulthana, A. Jaithunbi, A.K. Sunraja, P. University of Greenwich Department of Computing and Mathematical Sciences Old Royal Naval College London United Kingdom RMD Engineering College Department of Computer Science and Engineering Kavarapettai India SRM Institute of Science and Technology Department of Computer Science Kattankulathur India

ISBN: (纸本)9781665494007

Healthcare services save the life of human beings by making timely effective decisions. The use of data mining tools is crucial for decision making, forecasting, and disease prediction. In this study, data mining algorithms are applied to predict heart disease. The dataset contains 14 attributes such as age, gender, blood pressure, blood fat, etc. These parameters are analyzed to predict the probability of patients prone to heart disease in future. Initially, the relationship between the parameters is analyzed. Following which Naïve Bayes, decision trees and Naïve Bayes with k-means clustering are applied over it for classification and prediction. These algorithms were employed to train the dataset and create a binary classification. The proposed system shows a better prediction of heart disease. The performance measures of the system are measured, and the obtained results illustrate the system can forecast the probability of developing the heart diseases. © 2023 IEEE.

关键词： Forecasting

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Integrating Big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big data and conventional soil science practices.. © 2025 IEEE.

关键词： data quality

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Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications 3rd

Performance of Machine Learning Classifiers for Anomaly De...

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3rd international conference on advances in computing Research, ACR 2025

作者： Haug, Markus Velarde, Gissel IU International University of Applied Sciences Erfurt99084 Germany

ISBN: (纸本)9783031876462

This work empirically evaluates machine learning models on two imbalanced public datasets (KDDCUP99 and Credit Card Fraud 2013). The method includes data preparation, model training, and evaluation, using an 80/20 (train/test) split. Models tested include eXtreme Gradient Boosting (XGB), Multi Layer Perceptron (MLP), Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Multiple-Objective Generative Adversarial Active Learning (MO-GAAL), with XGB and MLP further combined with Random-Over-Sampling (ROS) and Self-Paced-Ensemble (SPE). Evaluation involves 5-fold cross-validation and imputation techniques (mean, median, and IterativeImputer) with 10, 20, 30, and 50 % missing data. Findings show XGB and MLP outperform generative models. IterativeImputer results are comparable to mean and median, but not recommended for large datasets due to increased complexity and execution time. The code used is publicly available on GitHub (***/markushaug/acr-25). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Corporate Social Responsibility Reports: A Big data Approach 3

Corporate Social Responsibility Reports: A Big Data Approach

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Pacori Paricahua, Edwerson William Karina Pacori Paricahua, Angelica Gehlot, Anita Patidar, Vijay Kumar, M.V.Suresh Lourens, Melanie Universidad Nacional de Juliaca Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Department of AERO India Koneru Lakshmaiah Education Foundation Department of Law Vaddeswaram Guntur 522302 India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa

ISBN: (纸本)9798350399264

The environmental consumption in the businesses related to discharge of waste into water land and air as well as the working conditions of the employees at the workplace are considered best interest for the public. Because of the marketing considerations pressure of public and competitive environment the parameters are required to do so by laws and regulations, businesses attempt to minimize or mitigate the negative effects that are associated with the value generating processes. These upgrades are frequently included in Corporate Social Responsibility (CSR). Many businesses establish CSR goals for themselves that are measured against a specific standard. By doing this, the businesses can identify the areas that need improvement, track their advancement, use various approaches the steps are developed for further improvement in the situation. Few of these indicators are influenced by the digitalization of technologies, and as this field of study advances, there are more ways to lessen the negative effects. It is commonly overlooked how much room digitization has to improve CSR metrics. Investment in area of application may be justified for big data potential and analytics and the generation of corporate social responsibility report under the various parameters are evaluated in the research study. The novelty of the present study is using big data analytics in CSR report through the designed framework for collection of data and big data capabilities for the creationof sustainable healthy environment globally. © 2023 IEEE.

关键词： Big data

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3rd EAI international conference on Edge computing and IoT, EAI ICECI 2022

3rd EAI International Conference on Edge Computing and IoT, ...

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3rd EAI international conference on Edge computing and IoT, EAI ICECI 2022

ISBN: (纸本)9783031289897

The proceedings contain 21 papers. The special focus in this conference is on Edge computing and IoT. The topics include: Evaluation of Higher Education System;defense Mechanisms Against Audio Adversarial Attacks: Recent advances and Future Directions;an Empirical Study of Worldwide Plastic Waste Mitigation;prediction for Surface Subsidence of Shield Construction in Water-Rich Sand Egg Stratum Based on Edge Intelligence;highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence;adversarial Example Attacks in Internet of Things (IoT);training Node Screening in Decentralized Trusted Federated Learning;exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge computing;federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices;the data Exchange Protocol over Multi-chain Blockchain Using Zero-Knowledge Proof;a Co-caching Strategy for Edges Based on Federated Learning and Regional Prevalence;LSTM-DAM: Malicious Network Traffic Prediction for Cloud Manufacturing System;a Hybrid Task Offloading and Service Cache Scheme for Vehicular Edge computing;on Enhancing Transmission Performance for IoV Based on Improved Greedy Algorithm;Precise Segmentation on Poly-Yolo, YoloV5 and FCN;Research on DAG Based Consensus Mechanism for Adjustable Load Metering data;a Detection and Information Processing Model Based on an Automatic Mechanism for Tax Payment Control in Developing Countries;IEC-FOF: An Industrial Electricity Consumption Forecasting and Optimization Framework;demons Hidden in the Light: Unrestricted Adversarial Illumination Attacks.

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Spatial Domain Method for Image Analysis: A Grey-Level Computation Approach 7th

Spatial Domain Method for Image Analysis: A Grey-Level Compu...

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Proceedings of the 7th international conference on advances in computing and data sciences, icacds 2023

作者： Deepika, Kumari Punj, Deepika Jyoti Atur Centre Model Colony Pune411016 India J C Bose University of Science and Technology 6 Mathura Rd Sector 6 Haryana Faridabad121006 India

ISBN: (纸本)9783031379390

This paper discusses image enhancement, which involves adjusting captured images to improve their visual quality and suitability for display or analysis using techniques like filtering, histogram equalization, and contrast adjustment. The spatial domain method is used for manipulating image grey levels, and the study analyses the transformed images using BRIQUE, NIQE, and PIQE quality metric descriptors. The study shows that the type of image used for processing depends on the application, and binary images have the highest PIQE score. Power law transformation or Gamma-corrected images can reduce or enhance contrast, depending on the gamma’s value. The transformed images have different scores based on the quality metric used. The study also finds that the sum of the global threshold values of an input image and its inverse is 1, and the scores for binary images using both thresholds are similar. Finally, the study shows that BRISQUE and PIQE scores are higher for gamma and more significant than 1;NIQE scores are similar for both gamma values. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction 3

Comparative Analysis of Machine Learning Techniques for Hear...

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3rd international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2023

作者： Susitha, N. Selvi, H. Mahesh Kumar, N.B. Nagarajan, V.R. Ali, Syed Sumera Kumar, L. Sathish TamilNadu Chennai India Saveetha College of Liberal Arts and Sciences TamilNadu Chennai India School of Computing Science and Hindusthan Institute of Technology Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Faculty of Arts Science Commerce and Management Tamil Nadu Coimbatore India Csmss Chh Shahu College of Engineering Maharashtra Aurangabad India Vit Bhopal University Scse Madhya Pradesh Kothrikalan India

ISBN: (纸本)9798350306989

Due to its substantial effects on society, efforts to enhance heart disease diagnosis and treatment have increased. Through data mining and the archiving of medical records, improved patient management is now possible thanks to the fusion of technology and medical diagnoses. Understanding the interaction between risk factors in the histories of patients and their impact on their prospects for heart disease is of utmost importance. This study aims to accurately predict cardiac disease by analyzing various data elements from patients. The selection and feature extraction methods are the most efficient choices for prediction system for heart disease. The following variables, such as age, sex, occupation, smoking, obesity, diet, exercise, mental stress levels, type of chest pain, history of chest pain, pressure, ECG, and results, have all been identified as significant factors in diagnosing heart disease. Various machine learning methods, such as Multilayer Perceptron (MLP), Naive Bayes (NB), K-nearest Neighbor (K-NN) and Support Vector Machine (SVM) were utilized to analyze the heart disease dataset. These datasets consisted of one with all features and another with only selected features. The objective was to compare the performance of these methods and determine which ones yielded accurate predictions. The results revealed that random forest, using the chosen features, outperformed other artificial intelligence algorithms, including those using all input features, achieving a maximum accuracy rate of 90%. As a support framework for predicting cardiac disease in its early stages, the suggested approach shows promise. This study advances the prognosis of cardiac disease, enables prompt therapies, and enhances patient outcomes by fusing the strength of data-driven AI algorithms with thorough feature selection. © 2023 IEEE.

关键词： Forecasting

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2019 3rd international conference on Intelligent computing in data sciences, ICDS 2019

2019 3rd International Conference on Intelligent Computing i...

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3rd international conference on Intelligent computing in data sciences, ICDS 2019

ISBN: (纸本)9781728100036

The proceedings contain 80 papers. The topics discussed include: optimizing electrical energy consumption in cloud data center;ams solar energy prediction: a comparative study of regression models;non-invasive health control through guided shopping: a rule-based approach;a new model based on global hybridization of machine learning techniques for 'customer churn prediction';a comparative study of similarity measurement in a new neural model of unsupervised clustering;EKF and UKF methods for the acoustic emission source localization in concrete;and deep learning based topics detection.

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