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Privacy-Preserving data mining Methods for Sensitive Information 3

Privacy-Preserving Data Mining Methods for Sensitive Informa...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Sukhvinder Jasim, L.H. Dari Dhote, Vijay Suseela, D. Venkatasubramanian, R. Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study explores innovative Privacy-Preserving data mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world. © 2023 IEEE.

关键词： Analytically Valuable patterns data Privacy data Repositories Homomorphic Encryption and Utility Privacy-Preserving data mining Sensitive Information

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3rd international conference on Artificial Intelligence and Knowledge Processing, AIKP 2023

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Knowledge Processing, AIKP 2023

ISBN: (纸本)9783031686160

The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge Processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Artificial Intelligence and Its Implementation? An Exploratory Study;anomalous Sound pattern Detection for machine Health Monitoring;performance Evaluation of Various machine learning Algorithms for Lung Cancer Prediction Using Demographic data;enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization;Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes;breast Cancer Diagnosis from Ultrasonic Image and Histopathology Image Using Deep learning Approach;Advancing Time Series Forecasting: LSTM Networks with Multiple Attention Mechanisms;trajectory Tracking and Navigation Model for Autonomous Vehicles Using Reinforcement learning;quantum Graph Neural Networks Based Protein-Ligand Classification;comparative Analysis on Speech Driven Gesture Generation;enhancing Deep learning: Leveraging Skip Connections and Memory Efficiency;quality-Based Decision-Making Using Image Processing for Supply Chain Management;enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced Vision Transformer Architecture;sweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets;a Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth;automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View;measuring the Vehicle-in-Motion, Density and Allocation of Traffic Signal Using Transfer learning;Ensemble Model of VGG16, ResNet50, and DenseNet121 for Human Identification Through Gait Features;Performance of Sentiment Analysis APIs on Political Opinion Polling;summarization of Telugu Text Discourses;crowd-Sourced Supervisors for the Automatic Invigilation of Online

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Research on Traffic sign recognition based on CNN Deep learning Network 3

Research on Traffic sign recognition based on CNN Deep Learn...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Qiao, Xiaoxuan Beijing University of Technology Beijing China

With the continuous development of social economy, the road traffic system as one of the basis of national economy is also increasingly developed, which is followed by the traffic safety problems are increasingly prominent. Among them, driving violations such as not obeying traffic signs are the main cause of traffic accidents. In this research, Traffic Sign recognition (TSR) has become a hot research topic. The research status and application field of traffic sign detection and recognition technology are introduced and analyzed in detail, and the improvement of traffic sign database is introduced. After that, the application of CNN in image processing and traffic sign recognition is introduced, and the existing problems in traffic sign recognition are analyzed. The CNN deep learning network used in this paper can conduct training while identifying targets, effectively improve the recognition accuracy of traffic signs under complex natural conditions and reduce the time consumption. © 2023 Elsevier B.V.. All rights reserved.

关键词： Traffic signs

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Predicting Coronary Heart Disease Through machine learning Algorithms 3rd

Predicting Coronary Heart Disease Through Machine Learning A...

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3rd international conference on Innovations in Computing Research, ICR 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafillou, Vassilios Electrical and Computer Engineering Department University of Peloponnese Patras Greece Health Physics and Computational Intelligence Lab Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation Lab Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9783031655210

machine learning has gained popularity in medical fields due to the increasing availability of health data and the improvement of machine learning algorithms. It can be used to create predictive models that diagnose diseases, predict disease progression, tailor treatment to individual patient needs, and improve the functioning of medical systems. The right use of data can have a positive impact on improving the quality of patient care, reducing healthcare costs, and creating tailored and effective medical approaches. The healthcare sector benefits greatly from the accurate interpretation of medical data as it contributes to the early prediction of diseases in patients. Early detection of a disease can help control the symptoms and provide the correct treatment. In our work, we analyzed actual measurements from the Framingham Heart Study and we created a medical database with 78001 records. Our ultimate goal is to develop an expert Artificial Intelligence system and an Artificial Neural Network that can predict the development of coronary heart disease by employing intelligent knowledge-mining algorithms. We have created two intelligent systems that predict the progression of coronary heart disease using machine learning algorithms such as Random Forest, Decision Trees and Neural Networks. In our experimental analysis, the Decision Tree and Neural Network achieved an accuracy of 90.08% and 84.56% respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis 3

Exploring the Potential Benefits of Machine Learning for Opt...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Ravindra Sakhare, Nitin N. Thangarasu, N. Senthilkumar, K.K. Wawage, Pawan S. Rengarajan, A. Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms. © 2023 IEEE.

关键词： machine learning

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A Survey on Wireless Network Traffic Analysis using machine learning Algorithms 3

A Survey on Wireless Network Traffic Analysis using Machine ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Praveen Kumar, M. Ashwitha Noble, P. Esha Malavika, V.S. Geethanjali, G. Farheen, A.S. Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

Analyzing and forecasting network traffic for different applications has gained popularity in recent years. Several tests have been carried out to find issues with the computer network apps that are in use today. Anticipating and evaluating network traffic can assist in implementing preemptive steps to guarantee safe, dependable, and excellent network connection. Numerous methods, including data mining approaches and neural network-based techniques, have been suggested and assessed for the purpose of predicting patterns of network traffic. For this goal, other linear and nonlinear models have also been developed. The main objectives of combining different network analysis and prediction techniques are efficiency and optimization. The goal of this survey study is to present a thorough review of the variety of methods used in traffic prediction and network analysis. The paper delves into the distinctive features and findings of previous research efforts and brings to light the patterns that have emerged from these studies. Furthermore, it provides an overview of the various areas in which network traffic analysis and prediction have shown their effectiveness. © 2023 IEEE.

关键词： data mining Techniques dataset NTA Preprocessing

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Citrus Detection Application of Based on Improved YOLOv3 Algorithm 3

Citrus Detection Application of Based on Improved YOLOv3 Alg...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Long, Jinhui Wang, Xiaokan Henan Electrical and Mechanical Vocational College Xinzheng451191 China

ISBN: (纸本)9781510664814

In order to solve the problem of missed detection and false detection caused by light change, leaf occlusion, fruit overlap and other citrus detection factor, an improved YOLOv3 citrus detection depth learning algorithm is proposed to achieve accurate and rapid recognition of citrus in natural environment. The algorithm uses image scaling and rotation transformation, homomorphic filtering processing and other methods to process the citrus object pictures. The 1− IOU value is the distance function, and the genetic algorithm based on K-means clustering is used to optimize the initial anchor box size, all that are enriched the citrus image data set. So the algorithm achieves effective image enhancement and improves the detection resolution and accuracy. The experimental results show that the average accuracy of the citrus detection algorithm reaches 88.7%, the average detection time of single width pixels 640×448 pixels is 131 ms and the system efficiency increases by 9.6%. © 2023 SPIE.

关键词： Genetic algorithms

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image Processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine learning Model for Traffic Prediction and pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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2023 3rd international conference on Applied Artificial Intelligence, ICAPAI 2023

2023 3rd International Conference on Applied Artificial Inte...

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3rd international conference on Applied Artificial Intelligence, ICAPAI 2023

ISBN: (纸本)9798350328929

The proceedings contain 11 papers. The topics discussed include: metaheuristic firefly and C5.0 algorithms based intrusion detection for critical infrastructures;a novel spatial-temporal deep neural network for electricity price forecasting;temporal-spatial graph neural network for wind power forecasting considering the blockage effects;European standardization efforts from fair toward explainable-AI-ready data documentation in materials modelling;detection of pneumonia from chest X-ray images using convolutional neural network (CNN);SVM at edge: low cost caching prediction for connected edge intelligence in federated machine learning;staying ahead of the curve: early prediction of academic probation among first-year cs students;facial expression recognition using deep neural network;human posture detection on lightweight DCNN and SVM in a digitalized healthcare system;and fishing trawler event detection: an important step towards digitization of sustainable fishing.

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Research on the High Resolution Remote Sensing Image Target Detection Based on machine learning 3rd

Research on the High Resolution Remote Sensing Image Target ...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Fu, Yanli Sun, Yingying Li, Shuyao Deng, Rui Linlin, Hou Engineering University of CAPF Shaanxi Xian China

ISBN: (纸本)9783031631351

With the development of computer technology and the national aerospace industry, remote sensing satellites have been successively projected into the air, and the resolution of remote sensing images is becoming higher and clearer. The surface information contained in remote sensing images can be used not only in civil fields such as forestry, agriculture, water conservancy, but also in the military and national defense fields. Current target detection technologies have achieved good results in natural image detection, but their performance in remote sensing images is poor. This is due to the high resolution of remote sensing images themselves, the small size of key targets in the image, and the dense distribution in multiple directions, while the background information is very complex and covers the entire range of the image, resulting in low detection accuracy and slow speed issues when using traditional methods for target detection. High resolution remote sensing images have a wide range of utilization scenarios. The traditional remote sensing image target detection based on artificial vision recognition and processing has been difficult to effectively cope with and meet the practical needs of large-scale high-resolution remote sensing impact target recognition. Based on this, this paper first analyzes remote sensing image information extraction methods based on machine learning, then studies high-resolution remote sensing image target detection methods based on machine learning, and finally presents the development trend of high-resolution remote sensing image target detection based on machine learning. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Remote sensing

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