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A Technique Based on Ensemble machine learning for The Analysis of Electronic Nose Signals

A Technique Based on Ensemble Machine Learning for The Analy...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Amit Taneja Santhosh Kumar Rajamani Deepika Shekhawat Yerrolla Chanti Kapil Joshi Ashish Garg AIT-CSE Chandigarh University Department of E.N.T MAEER MIT Pune’s MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Pune Maharashtra India Department of Computer Science & Engineering Manipal University Jaipur Rajasthan School of Computer Science and Artificial Intelligent SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs)—reliefF, chi-square, and Gini index—for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.

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A Hybrid Approach for Intrusion Detection System using data Minining and Artificial Neural Network 3

A Hybrid Approach for Intrusion Detection System using Data ...

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Pathania, Anjali Ghai, Vivek Chandigarh University Computer Science Department Candigarh Mohali India

ISBN: (纸本)9781665438117

This paper is going to present a hybrid model for IDS using ANN and data mining approaches. it is important to deploy a hybrid network using ANN based and data mining based IDS systems that can enable the system in making decisions on intrusion in a mobile environment. MANET is an infrastructure-less ad-hoc network. It is a collection of mobile nodes that are connected in an arbitrary and dynamic manner. ANN (Artificial Neural Network) is a type of machine learning scientific and statistical model that is used by computer systems in order to perform a task without involvement of explicit intrusion, rather than relying on interference and patterns instead. As there is node mobility that can impose different security issues, MANETs are found more susceptible in security provision. In order to resolve this security issue, some authentication and encryption techniques can be proposed for the first-line defensein order to mitigate the security risks. However, complete eradication of these types of risks is next to impossible. For theSecond line defense, the necessity of an Intrusion Detection System (IDS) is essential in this case. IDS can be referred as a method tool or resource that can help to detect access and warn for any activity of unapproved or unauthorized network activity. The current research has been done to prevent the network from the intruders. The number of attackers has been increased in the network because of more usage of technology and wireless network. data is not allowed by the attackers to transmit successfully from source node to destination node. These intruders want to steal data and let to the unsuccessful transmission. In this research we are deploying 48 to 66 nodes in a defined communication area of 1000*1000 height and width. While starting the transmission source node and destination node is defined. After that while using IDS and AODV protocol route is created between destination and source node. If network decreases its performance will

关键词： data mining

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Performance Assessment of Various machine learning Classification Methods for Classifying the Landcover Using Landsat 8 OLI

Performance Assessment of Various Machine Learning Classific...

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Intelligent Technologies (CONIT), international conference on

作者： Auchithya Sajan Dhanya M Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

Classification is a technique used for categorizing different features associated with the land cover. It is a very important and visually distinguishable method for understanding the land cover and land use pattern in any area. Classifying images is a part of every study that includes change detection. Various methods are used for classification. Classical, conventional and parametric methods of classification are always time consuming and chances for error are also high. Supervised and unsupervised classification techniques are useful inorder to classify the images into distinguishable forms. For fast and accurate classification it is very vital to build a model that can classify the satellite image. In this study two machine learning techniques are used to perform LULC classification and the accuracy of both techniques are compared and a performance assessment is done. For attaining the best classification results it is important to provide good training samples, correct data sets, the best algorithm for classification, etc. So here we use Landsat 8 Operational Land Imager (OLI) dataset and uniform training sites for all three classification techniques. Maximum Likelihood Classification (MLC) is done in ArcGIS software, Naive Bayes classification (NBC), and Random Forest Classification (RFC) is done in Google Earth Engine using the same training samples. All three classifiers worked well. To evaluate the accuracy of each classifier, the Kappa coefficient was calculated for each technique by forming a confusion matrix. The accuracy assessment gave a promising result that Random Forest is comparatively the best technique with an accuracy of 94.86% while NBC and MLC also gave satisfactory accuracy values. All three classification techniques applied had certain limitations and were not 100% accurate hence study can be elaborated to find more classifying techniques and their accuracy in order to get the most reliable classification technique.

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Deep learning Envisioned Accident Detection System 1

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Alam, Intekhab Verma, Ayush Khari, Manju Jawaharlal Nehru University New Delhi110067 India

ISBN: (数字)9783031530821

ISBN: (纸本)9783031530814

The conflict between computational overhead and detection accuracy affects nearly every Automated Accident Detection (AAD) system. Although the accuracy of detection and classification approaches has recently improved dramatically, the systems’ high computational resource requirements make them unsuitable for deployment in situations when immediate feedback is required. This paper suggests a strategy for developing an accurate, cost-efficient automatic collision identification system that can be deployed with a minimal amount of hardware. The AAD system is divided into three key phases—Detection, Tracking, and Classification and offers computationally reduced intensive algorithms for each of these stages. For the detection phase, YOLOv3, it uses a deep learning model that has been trained through knowledge distillation. Its accuracy is on par with that of YOLO (You-Only-Look-Once). On the basis of the information inference and data mining techniques COCO (Common Objects in Context) dataset, YOLOv3 surpasses all other identification techniques considering runtime complexity, averaging an astounding 30 frames/second on a low-end system. It attains an Average Precision (AP) mark of 44.8. For the tracking phase, the system makes use of SORT (Simple Online Real-time Tracking). Since a radial basis kernel with a support vector machine works efficiently, an area under the curve (or AUC) score of 0.92 is achieved. Alongside this, this paper examines a number of machine-learning techniques for the classification stage. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Research on the Fusion pattern recognition System Based on the Concept of Production Education Integration and Application of Generative Countermeasure Network 1

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3rd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2021

作者： Lv, Conggang Jiangxi Tourism and Commerce Vocational College Nancang330039 China

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

In order to highlight the practical application value of network data and information fusion behavior under the background of industry-education integration, a fusion pattern recognition system that applies generative confrontation network under the concept of industry-education integration is designed. First, the cyclic neural network is used to generate independent text information packets. While establishing the generation of the confrontation network framework, various reinforcement learning parameters are adjusted to realize the construction of the hardware execution environment of the recognition system. On this basis, build an embedded network framework, with the help of EEPROM chip and LD3320 chip circuit, to supervise the fusion process of network data information identification and implementation behavior, and realize the construction of the system’s software execution environment. Combined with the related hardware equipment structure, complete the research on the fusion pattern recognition system of the application generation confrontation network under the concept of integration of production and education. Comparative experiment results show that with the application of the above system, the mean value of network data information fusion time is reduced from 17.9 s to 11.2 s, while the maximum amount of information processed by a single fusion process reaches 9.3 × 1012T which can be used in the context of the integration of production and education Effectively highlight the practical application capabilities of network data information fusion behavior. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Integration

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Method for Recognizing Hand Gestures in Real- Time Across Multiple Sensors Using machine learning

Method for Recognizing Hand Gestures in Real- Time Across Mu...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Jayanthi M Rajeswari. S Department of Computer Science and Engineering Kalaignar Karunanidhi Institute of Technology Coimbatore India

The field of human-computer interaction includes multimodal gesture detection as a key task. In human-machine interface, wearable sensors for hand gestures consume additional potential than ambient detection due to their short price, weightiness, and wide applicability. Despite the fact that research on hand motions with wearable sensors already exists, the results are rarely transferable from one scenario to another. And when it comes to collaboration between humans and technology, there is still no agreed-upon protocol for hand signals. In this study, we introduce a new technique of using hand gestures to command the environment. An affordable Internet of Things (IoT) sensor-pluggable device that has an integrated accelerometer and gyroscope is used to collect the data for analysis. Inputs to the feature extractor network include the raw data from the RGB sense modality, the visual flow sense modality, and the disassembled information from the hands. This work provides an additional modality, feature extraction based on temporal differences in RGB sense modality. The characteristics of these modes are learned using a pre-trained version of the Googlenet Caffe model. All of these components need to come together as a whole for the fusion procedure to be successful. When doing a classification, the bidirectional LSTM network is used. The new approach is put through its paces using the IPN Hand dataset. When first starting out, each modality is employed independently. Then, we employ such mixtures to boost classification precision even further. Combining the segmented indicator sense modality with the optical movement mode and the chronological transformation of the RGB sense modality yielded the greatest results. The suggested method for multimodal gesture recognition outperforms the benchmark methods, as shown by the simulation results

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A Real-time Face recognition Smart Attendance System with Haar Cascade Classifiers 3

A Real-time Face Recognition Smart Attendance System with Ha...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Bairagi, Rupak Ahmed, Remon Tisha, Sadia Afrin Sarder, Md. Sumon Islam, Md. Sabiqul Islam, Md. Ashiqul Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9780738146270

Taking attendance of students during class time is one of the foremost tasks for teachers and it's pretty much complicated at the same time. The manual attendance system takes an enormous amount of time over the number of students and has a prospect of being a proxy. Day by day it's getting intimidating because the number of students is increasing. A previous couple of year's automated biometric systems like a fingerprint, QR-code technology is using in smart attendance systems. However, time makes the difference here, at present face recognition technology is using to identify the student's participation in the classroom. So, we proposed a smart attendance system to take attendance by detecting and recognizing the face. Our main motivation is to make the attendance system easier, less time-consuming, and also protect from being proxy. The old manual-based attendance system was an arduous process and had a chance of proxy but we will be able to ameliorate the situation. This system can make a crystal clear concept to the machine whether it's a legal attendance or proxy. This system is a more secure and hassle-free system. In this system, all the student's data are recording in the cloud database with time and class schedule. So that teachers can easily evaluate the students in marking on attending. Also, help the teacher to inform individuals' parents whether or not their son/ daughter is irregular in the class. In this system, we used an effective machine learning-based object detection method Haar Cascade classifiers proposed by Paul Viola and Michael Jones for classifying extracting images and recognizing LBPH to this system. © 2021 IEEE.

关键词： Face recognition

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Dual-Channel Decoupling Marching with Density Relationship Global Fusion for Few-shot Object Detection

Dual-Channel Decoupling Marching with Density Relationship G...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Futian Wang Yu Yi Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China AHU-IAI AI Joint Laboratory Anhui University Hefei China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Traditional deep learning approaches to object recognition typically require large datasets, and acquiring high-quality annotated data prohibitively expensive. Consequently, there has been a growing interest in few-shot object detection, which aims to classify and detect novel classification with only a limited number of samples. Existing methods do not fully exploit feature matching between support images and query images. We propose called the dual-channel decoupled matching with density relationship global fusion method for few-shot object detection. The dual-channel decoupled matching backbone network is specifically designed to mitigate the mutual interference between support features and query features, enabling the extraction of diverse information and improving the model's generalization capability. In order to perceive a more comprehensive feature information, the density relationship global fusion module utilizes feature information of different scales to obtain a more comprehensive feature representation. Extensive experiments conducted on the PASCAL VOC datasets validate the efficacy of our proposed method.

关键词： Deep learning Computer science Object detection Interference Feature extraction Object recognition data mining

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Graph Neural Networks for Functional Connectivity Analysis: Uncovering Neural Network Disruptions in Parkinson’s Disease

Graph Neural Networks for Functional Connectivity Analysis: ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： R. Ayyappa C. H. Hussaian Basha S. Senthilkumar K. Balasubramanian S Arunkumar S. Venkatesh Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering SR University Telangana India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the field of Parkinson’s’ disease diagnosis and progression monitoring, functional and structural connection patterns in the brain, which can be observed by modalities such as functional magnetic resonance imaging (MRI) and electroencephalography (EEG) are becoming more recognized as promising bio markers. An innovative use of Graph Neural Network (GNNs) is proposed in this study for analyzing patterns of brain connectivity. By utilizing the graph structure of brain networks, the researchers identify disturbances that are unique to Parkinson’s disease conditions. In the proposed framework, brain regions are represented as nodes, and the edges of the graph correspond to functional or structural connections derived from either fMRI or EEG data, depending on the modality used for analysis. The investigation of the brain regions and the connection that are most pertinent to the diagnosis can be accomplished through the utilization of attention weights in feature importance analysis. In recognizing brain network changes in Parkinson’s disease, the findings indicates that the GNN perform much better than traditional machine learning methods. Additionally, these research offers significant advancements into mechanism of neuronal degeneration leads attention to certain regions of the brain and connected throughout the brain that are impacted by Parkinson’s disease. This work contributes to a more comprehensive knowledge of neural network disruptions in neurodegenerative disease and enhances the potential of GNN as diagnostic tool.

关键词： Accuracy Functional magnetic resonance imaging Motors Graph neural networks Electroencephalography Telecommunication computing Planning pattern recognition Biological neural networks Diseases

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A Study on machine learning Algorithms: Forecasting Crop Prices 3rd

A Study on Machine Learning Algorithms: Forecasting Crop Pri...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Hirpara, Jignesh Doshi, Milan Patel, Amit Choudhury, Koushik Marwadi University Rajkot-Morbi Road Gujarat Rajkot360003 India Department of C.S. & I.T Atmiya University Rajkot Gujarat India

ISBN: (纸本)9783031862984

In this era, with the technology used in every field, the agriculture market also generates a huge amount of revenue every day. Computer technology plays a vital role in managing and finding meaningful information about it. To manage large datasets, a field that integrates data mining, machine learning, mathematics, computer applications, and artificial intelligence is used. Crop price forecasting is a demanding and interesting method for the agricultural sector because it is dependent on future crop production worldwide. The maximum crop price for the current session or the next year is something that the entire agriculture community is interested in knowing. Currently, crop price analysis dominates the study instead of agriculture crop amount forecast. In comparison to other nations, crop yield in India is quite uneven when measured by the agriculture fraternity. It is insufficient as a base rate for any chosen crop price if an appropriate MSP is not provided. There may be a decrease in Indian poverty when these farmers receive or determine fair crop prices. A vast amount of commodity data is produced in agriculture today. A significant amount of commodity data is produced by the agriculture sector, but sadly, most of this data is not utilized to uncover hidden information. machine learning algorithms are used to make a machine think like a human, comprehend the data sets that are currently available, and compare them to similar events that have happened in the past. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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