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检索条件"主题词=Machine Learning Algorithms"

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Diabetes Prediction Using machine learning and Hybrid Deep learning Ensemble Technique

Diabetes Prediction Using Machine Learning and Hybrid Deep L...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Pranesh Chowdhury Pratham Barua Mohammed Nazim Uddin Department of Computer Science & Engineering East Delta University Chittagong Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Diabetes prediction is an essential task of healthcare which enables early diagnosis and treatment. In this study, we present a comprehensive analysis utilizing machine learning and ensemble deep learning techniques for diabetes prediction, leveraging two distinct datasets: such as the PIMA diabetes dataset which contains 768 records with 9 attributes, and another diabetes dataset which has exactly 2000 records with 9 attributes. The approach that we have adopted for cleaning up data has a lot of prerequisites, aimed at achieving accuracy. We applied a preprocessing technique for removing duplicates and replacing zero values with the mean or median. In addition to that, we applied the scaling process known as standardization on our given data, it is a method that ensures the average of all the values is 0 and the standard deviation is 1. To process our input finding, we implemented a set of machine learning algorithms covering K Nearest Neighbors, Naive Bayes, Support Vector machine, Decision Tree, Random Forest, and Logistic Regression along with ensemble deep learning techniques of TabNet, XGBoost, and Multilayer Perceptron which shows the best possible outcomes. The proposed hybrid deep learning ensemble approach presents the best results for predicting diabetes. In our research hybrid deep learning ensemble technique has accuracy of 96%, this method shows much higher efficiency over common machine learning algorithms.

关键词： Deep learning Support vector machines Accuracy machine learning algorithms machine learning Medical services Predictive models Prediction algorithms Diabetes Random forests

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Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, Landsat 9 TIRS, and PlanetScope Imagery with machine learning and Data Fusion

Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, L...

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International Conference on Computer and Applications (ICCA)

作者： Souleymane Bah M. D. Tanguy Mesreil Houegbe Emmanuel Wilfried G. Megnigbeto Vinasetan Ratheil Houndji Bignon Nicanor Kouton N. Gérard Gouwakinnou C. Eugène Ezin Institut de Mathématiques et de Sciences Physiques Université d'Abomey-Calavi Dangbo Bénin BiPLAC Laboratoire d'Ecologie de Botanique et de Biologie végétate Parakou Benin Division protection phytosanitaire Institut de Recherche sur le Coton Parakou Bénin Institut de Formation et de Recherche en Informatique Université d'Abomey-Calavi Abomey-Calavi Benin

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helping to reduce the use of pesticides. Given that traditional field inspection requires considerable effort with limited effectiveness, remote sensing combined with machine learning is an essential complement for large-scale, efficient monitoring. This study therefore aims to develop a machine learning-based solution for detecting cotton jassids from free satellite imagery data in Alibori, northern Benin. Multispectral and thermal infrared images respectively from the Sentinel-2 and Landsat 9 satellites were fused in the most correlated bands using signal processing algorithms Pseudo Wigner Distribution (PWD), Nonsubsampled Contourlet Transform (NSCT), and Discrete wavelet transform (DWT). Two configurations, in terms of data augmentation, were used. Areas infested by jassids were identified in merged and unmerged satellite images (Sentinel-2, Landsat 9, and PlanetScope, a commercial satellite) using Support Vector machine (SVM), Random Forest (RF), and CatBoost supervised machine learning algorithms. The green and Thermal Infrared Sensor 1 (TIRS1) bands are the most correlated in configuration 1 and the blue and TIRSI bands in configuration 2 for respectively Sentinel-2 and Landsat 9. The DWT and NSCT signal processing algorithms produced the least Root Mean Square Deviation (RMSE) for image fusion (0.46 for DWT and 0.33 for NSCT). The best result in terms of detecting performance was obtained with the composite treatment of Landsat 9 images and the RF algorithm, with an overall accuracy of 88.23%, an Fl-score of 82%, and a Kappa index of 79.92% in configuration 1 and in configuration 2 for composite treatment of Sentinel-2 an overall accuracy of 92.36%, an Fl-score of 90.75 % and a Kappa index of 86.36% with CatBoost algorithm.

关键词： Radio frequency Support vector machines Satellites machine learning algorithms Signal processing algorithms Landsat Discrete wavelet transforms Satellite images Cotton Monitoring

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Benchmarking machine learning based Intrusion Detection for IoT Edge Devices

Benchmarking Machine Learning based Intrusion Detection for ...

引用

International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Maram Alsharif Danda B. Rawat Department of Electrical Engineering & Computer Science Data Science and Cybersecurity Center (DSC2) Howard University Washington D.C USA

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

machine learning-based Intrusion detection (ML-IDS) gained a lot of interest from the research community as security threats facing the IoT are on the rise. Researchers focused on improving intrusion detection efficiency based on machine learning. However, the feasibility of applying proposed ML algorithms to resource limited IoT devices limited. We present a proof of concepts (POC) for ML-IDS for IoT edge device using actual devices. Furthermore, we envisage the lack of virtual environments that mimics the realistic IoT world, puts research completeness on bay. In this paper, we extend the benchmarks of intrusion detection, applied to IoT edge devices of a varying of computing resources. The benchmarks are based on machine learning techniques endorsed by the research community, and adopt popular intrusion datasets for training and testing models. We also utilize the benchmarks to calibrate Virtual-box environment in a novel way to produce virtual machines that closely mimic realistic IoT edge devices. Thus accurate virtualization would become a reliable tool available to researchers to test their approaches.

关键词： machine learning algorithms Image edge detection Computational modeling MIMICs Intrusion detection machine learning Benchmark testing Virtual machines Internet of Things Security

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A machine learning-assisted Game Theoretical Service Selection Strategy in UAV Networks

A Machine Learning-assisted Game Theoretical Service Selecti...

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Emerging Technologies (GCET), Global Congress on

作者： Abdessalam Mohammed Hadjkouider Chaker Abdelaziz Kerrache Yesin Sahraoui Asma Adnane Carlos T. Calafate Dpt. of Computer Science and IT Kasdi Merbah University of Ouargla Ouargla Algeria Dpt. of Computer Science Université Amar Telidji de Laghouat Laghouat Algeria National Higher School of AI Algiers Algeria Computer Science Department Loughborough University Loughborough UK Computer Engineering Department (DISCA) Universitat Politècnica de València Valencia Spain

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

The Unmanned Aerial Vehicle (UAV) has emerged as a transformative technology application in healthcare, environmental monitoring, and search and rescue operations, with the growing complexity of their services, Dynamic Environments, and limited resources. This paper introduces a novel approach that integrates machine learning (ML) with game theory to revolutionize the decision-making process in UAV service selection. Our method focuses on enhancing user experience by employing ML algorithms to analyze user requests and predict the most suitable UAV services. Concurrently, game theory is integrated to evaluate and ensure efficient selection regarding delay, throughput, packet loss ratio, and residual energy within UAV *** synergy aims to benefit both users and service providers by optimizing service delivery and user satisfaction. Experimental results demonstrate the efficacy of our model, highlighting its superior performance in terms of accuracy, precision, recall, and F-score.

关键词： machine learning algorithms Packet loss machine learning Medical services Autonomous aerial vehicles Throughput Prediction algorithms User experience Game theory Vehicle dynamics

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MCWOA based Hybrid Deep learning for Detecting the Attacks in Cybersecurity with IoT Network

MCWOA based Hybrid Deep Learning for Detecting the Attacks i...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sravanthi Dontu Rohith Vallabhaneni Santosh Reddy Addula Piyush Kumar Pareek Haider Mohammed Abbas Department of Information Technology University of the Cumberlands Williamsburg USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

All throughout the globe, machine learning is being used in a diversity of settings, from universities to businesses. Intuitive machine learning algorithms can assess dangers and react quickly to security breaches and problems. In the realm of cybersecurity, it is essential for providing a proactive security system. Cybersecurity safeguards data, systems, and networks against unauthorized access in real time. Multiple analyses of privacy and security metrics over the past decade have found that the frequency and severity of cybersecurity breaches have been steadily increasing. Criminals are penetrating data protection systems at a rapid clip. Top cyber-security problems that need efficient explanations include anomaly finding, software vulnerability diagnosis, phishing page proof of identity, malware identification. In addition, NSL-KDD or KDD-CUP99 datasets provide the basis of most extant research. None of these datasets contain assaults that occurred recently. Networkflow ToN-IoT, a realistic dataset obtained from a large-scale, heterogeneous IoT network, was therefore utilized in the study. The study used a hybrid deep learning model combining ResNet and AlexNet for classification purposes. This study employs a hybrid optimization method that takes cues from both the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA) to fine-tune AlexNet's parameters. In order to recover the accuracy of MCWOA's identification, the Sobol arrangement is employed during the initialization of the population to distribute it uniformly over the solution space. Additionally, the position update process of classic MCWOA is enhanced by combining the bubble-net hunting and random search processes of the whale optimization algorithm. The results have important consequences for the continuous creation of adaptable, efficient, and secure intrusion detection systems for the complicated environment of IIoT networks Hybrid (ResNet-AlexNet) model reached the accuracy a

关键词： Deep learning machine learning algorithms Accuracy Optimization methods Intrusion detection Whale optimization algorithms Classification algorithms Reliability Computer security Computer crime

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Palm Print Recognition Using DES Algorithm with machine learning

Palm Print Recognition Using DES Algorithm with Machine Lear...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： M.Sowmiya Manoj Harikaran M Aakashraj K Department Of Electronics And Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The data encryption algorithm of an e-commerce platform has a poor encryption effect. Because data is often lost and corrupted after encryption, this study proposes a data encryption technique along with biometric technology. Here, the encrypted text is given to the recipient along with his palm print data for authentication, and the encryption and decryption procedure is made more secure by employing the DES algorithm. The two associated techniques of digital envelopes and data authentication are investigated in order to secure data accuracy and one-time encrypting. Based on this, the process of symmetric encryption algorithm and palm print recognition method depending on convolutional neural network (CNN) are investigated, and the security is checked, in order to assure the effectiveness of encryption. The suggested technique can encrypt data from an e-commerce system, and the encryption procedure is fairly simple, secure, and authenticated, with a lower time complexity.

关键词： machine learning algorithms Palmprint recognition Neural networks Authentication machine learning Encryption Electronic commerce Convolutional neural networks Time complexity Standards

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Health Analysis and Prediction Using machine learning

Health Analysis and Prediction Using Machine Learning

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： B. Renukadevi K.S. Velmurugan S. Saravanan Department of Information Technology Sri Sairam Engineering College Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper proposes the development of an application aimed at monitoring dietary habits and promoting fitness through appropriate physical activity. In our modern automated world, where busy lifestyles often lead to unhealthy eating habits, such applications become necessary. Using machine learning techniques for segmentation, the application has modules for auditing, personalized meal planning, activity tracking, adding goals, and progress tracking Users their information, such as height and weight, is entered to provide personalized meal plans and exercise recommendations. The application generates progress reports and predicts health outcomes based on user data.

关键词： machine learning algorithms machine learning Prediction algorithms Telecommunication computing Planning Internet of Things Monitoring

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machine learning-Based Approach to Transcribing Language Units

Machine Learning-Based Approach to Transcribing Language Uni...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Oksana Kovtun Viacheslav Kovtun Department of Theory and Practice of Translation Vasyl’ Stus Donetsk National University Vinnytsia Ukraine Department of Computer Control Systems Vinnytsia National Technical University Vinnytsia Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

The article addresses the task of automated transcribing of speech signals within the framework of pattern recognition theory. To tackle this task, an adaptation of the decision tree method is proposed in the form of a sequence of tasks of multi-element statistical classification of phonemes in speech signals. The algorithm is controlled by two threshold values that determine the permissible divergence between the investigated and reference signals characterizing the same phonemes in terms of relative entropy metric, and restrict the maximum duration of phonation for the corresponding speech element. Experiments demonstrated both the functionality of the proposed algorithm and its adaptability, provided by the variation of the mentioned threshold values.

关键词： Measurement machine learning algorithms Speech recognition Transformers Entropy Classification algorithms Decision trees Information technology

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machine learning for Bacterial Growth Prediction and Examination of Bacterial Impact on Growth

Machine Learning for Bacterial Growth Prediction and Examina...

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International IEEE Conference on Intelligent Systems, IS

作者： Volkan Ince Mohamed Bader-El-Den Farzad Arabikhan Omer Faruk Sari School of Computing University of Portsmouth Portsmouth UK

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

In recent years, the growing concern about bacterial growth has become a significant issue in food safety and quality assurance. In this context, the increasing use of machine learning plays a crucial role in understanding and managing these microbial processes in the food industry. This study conducts a comprehensive analysis of bacterial growth in pork products by integrating machine learning methodologies with the high-throughput sequencing technology known as 16S rRNA metabarcoding, which is commonly used to characterize microbial communities, by estimating the relative abundance of microbes. The research investigates the detection and prediction of the growth of microbial populations on food, crucial for safe-guarding food safety and quality in the industry. Utilizing various machine learning algorithms, the study explores the relationship between bacterial quantities and tryptose-sulfite-cycloserine agar (TSC), which is used for the enumeration of Clostridium organisms in a variety of foods and food ingredients. Moreover, the integration of explainable AI (i.e., SHAP) analysis highlighted specific influential bacterial genera, particularly emphasizing the role of Clostridium in the context of TSC values. Additionally, in the study, feature reduction was performed using SHAP analysis. The initial set of features, consisting of 200 genera, was reduced to 8 genera, and the impact of the model was examined. In the comparison between these two different feature counts, the Multi-Layer Perceptron model achieved the highest the area under the curve value (0.85). The SHAP outcomes indicated that the MLP model heavily depended on the Carnobacterium genus, with the Clostridium genus emerging as the second most crucial genus in the model. This research emphasizes the significance of advanced methodologies in understanding microbial communities, contributing to the development of robust strategies for food safety and quality assurance in the food industry.

关键词： Industries Sequential analysis Microorganisms Quality assurance machine learning algorithms Explainable AI Food industry Safety Intelligent systems

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Prototype of Autonomous Vehicle by Using machine learning Techniques

Prototype of Autonomous Vehicle by Using Machine Learning Te...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Dhajvir Rai Vijay K Yadav Reetika Mehra Shobhit Goyal Department of Computer Science Engineering SoEC Dev Bhoomi Uttarakhand University Dehradun Lloyd Institute of Engineering & Technology Greater Noida Department of Management Quantum University

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

This In the modern world, automation is already at its peak but there is still lot more to explore. Self-Driving Cars are already in trend, which not only saves human effort but also drives at better efficiency. It adjusts to the changing environment at an effective rate and at a very low feedback error, which allow the car to adapt according to the road at a better rate. This ability of the self-driving Car leads to better performance as it can follow traffic and motor rules strictly. It also reduces accidents at road and eliminates poor driving abilities of the driver. In general, Raspberry Pi can follow a path using simple lane detection or any edge detector. The camera detects path, then in order to achieve our final goal commands are send to the motors based on the curve value present in the road. Curve values ranges from −1 to 1 and handles the code for the motor driver and other factors are also included to stop or start the motors of the car. These additional factors are based on the machine learning algorithms and other algorithm, which is coded to make the car work accordingly.

关键词： machine learning algorithms Roads Optical feedback Prototypes Motors Market research Autonomous automobiles Automobiles Optical sensors Vehicles

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