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A Novel Three Dense Layered Deep Fully Connected Neural Network for Hyperspectral Image Classification 1

3rd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2022

作者： Kanchanadevi, K. Shyamala Devi, M. Arun Pandian, J. Aruna, R. Palukuri, Suhas Kocherla, Uday Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (数字)9789811960888

ISBN: (纸本)9789811960871

With the advancement of satellite observations, the use of hyperspectral images has become more common. The accurate classification of ground features using hyperspectral images is an important research area that has received a lot of attention. The classification of hyperspectral images is a fantastic mechanism for analyzing diverse land cover in remotely sensed hyperspectral images. The need for the dimensionality reduction and inadequate data during training in the hyperspectral image remains a major challenge toward image classification in the remote sensing field. With this overview, a novel model is proposed for hyperspectral image classification as three dense layered deep fully connected neural network. The classification is done with logistic regression, SVM and decision tree, and the accuracy is compared with our proposed deep fully connected neural network model. Implementation results show that proposed three dense layered deep fully connected neural network model shows the accuracy of 95% compared to the existing classification algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Cricket Shot Classification and Pose Correction using Detectron2 and XGBoost Classifier

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Procedia computer science 2025年 258卷 1274-1284页

作者： Goutam Majumder Tanul Khatri Anubhav Tewari Atharva Samel Ganesh Khekare School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu India 632004

Real-time sports analysis provides valuable insights to teams, allowing them to change their tactics and increase their winning probability through strategising. In India, the globalisation of sports, specifically Cricket, is at its peak, due to the Indian Premier League (IPL). In a match, a team’s performance heavily relies on the individual performances of its players. This research paper proposes a model that internally utilises Detectron2, an AI tool, for pose detection. This model generates key points that are then used to recognize various batting shots and utilize the eXtreme Gradient Boosting (XGBoost) Classifier, achieving an accuracy of 92.13%. Furthermore, comparisons between the methods proposed and other researched methods show outperforming them. The proposed method has been tested on a dataset containing four (4) types of shots: pull shot, sweep, drive, and leg glance flick.

关键词： eXtreme Gradient Boosting Classification Convolutional Neural Network

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Securing Electronics Gadgets using Blockchain based Framework 2

Securing Electronics Gadgets using Blockchain based Framewor...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Prakash, P. N. Senthil Lanitha, B. Sundaram, Karthic Vellore Institute of Technology School of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331534950

The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency. © 2024 IEEE.

关键词： Ethereum

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Detection of Leading CNN Models for AI Image Accuracy and Efficiency

Detection of Leading CNN Models for AI Image Accuracy and Ef...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nayim, Meshal Mohan, Vishnu Pandey, Trilok Nath Dash, Bibhuti Bhusan Dash, Bibek Bikram Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to be University School of Computer Applications Bhubaneswar India HealthWorksAI Bangalore India

ISBN: (纸本)9798350360660

This research explores the challenge of distinguishing between authentic and AI-generated images using advanced convolutional neural network (CNN) architectures. Leveraging the CIFAKE dataset, which includes a diverse array of AI-generated and real images, we evaluated the performance of three pre-trained models: ResNet50, EfficientNetB0, and DenseNet121. Our study involved training these models with images of 32x32 and resized 64x64 pixel resolutions to assess their classification accuracy, precision, recall, and computational efficiency. DenseNet121 emerged as the most effective model, achieving an accuracy of 98.49% on the test dataset, significantly outperforming the other models. This work highlights the potential of deep learning methodologies in addressing the growing need for reliable AI image detection, providing a robust foundation for future advancements and applications in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart IoT Solutions for Subsurface Gas Monitoring and Safety in Critical Infrastructures 2

Smart IoT Solutions for Subsurface Gas Monitoring and Safety...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Chaganti, Pavan Chandra Vishal Sukesh, Koppuravuri Sai Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350340235

With increasing industrialization, everyone is learning about more life-threatening casualties. The recent few years have noticed a tremendous increase in critical conditions, eventually even leading to deaths and instant deaths of workers in sewers and manholes. These death reports and environmental concerns have been increasing at an alarming rate. Sewage gas is a combination of toxic and harmful gases. It mainly houses a vast variety of oxides of Carbon, Nitrogen and Sulphur, Ammonia, Methane which results from all the industrial wastes and household residues. Due to gases being trapped in manholes for prolonged periods, they become toxic and can be fatal if the right precautions aren't taken. Most of the time, light don't shine on these issues, but if they aren't handled, they could result in the occurrence of unfortunate events. Statistics show that there are numerous deaths due to gas poisoning from the toxic gases in these tunnels. Pits connecting to subterranean supply systems are critical to their maintenance because they affect communication networks, water supply networks, gas supply networks, and power networks, among other things. To deal with all these problems affected by these dangerous gases there is a smart IOT system prototype made that has been discussed in the paper. When there is a detection of a hazard gas, the users get notified with the help of actuators/sensors like buzzer, LCD screen through which they can be alerted. Although important for city operations, a hole can be one of the safest and most dangerous assets. © 2023 IEEE.

关键词： Arduino Wireless Networks Industrialization Monitoring System Subterranean

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An Effective EEG-Based Image Classification Methodology Under Low Data and Resource Requirements Using Machine Learning

An Effective EEG-Based Image Classification Methodology Unde...

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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Banerjee, Amardip Pal, Olivia Mondal, Mayukh Mukherjee, Avishek Chakrabarti, Partha Pratim Pal, Surjya Kanta Misra, Sudip Chakravarty, Debashish Indian Institute of Technology Kharagpur Mechanical Engineering Kharagpur721302 India Michigan State University Computer Science and Engineering 48824 United States Vellore Institute of Technology Computer Science and Engineering 632014 India Indian Institute of Technology Kharagpur Adv. Tech. Development Centre Kharagpur721302 India Indian Institute of Technology Kharagpur Computer Science and Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Mining Engineering Kharagpur721302 India

ISBN: (纸本)9798350386479

Deep Learning (DL) methods such as Convolutional Neural Network (CNN) learning approaches are used to extract features from images through convolution operation. These features are widely used for image classification, object detection or segmentations tasks. But a high testing accuracy needs lot of input images for training, whereas a human brain can do similar tasks with a few training samples. Present study is aiming to establish the potential of extracted features from electroencephalogram (EEG) signals acquired from brain superior to CNN extracted features for image classification tasks. For this, EEG signals are collected while seeing an image and thinking about its attributes through a questionnaire. A five-channel EEG headset has been used for data collection. Four standard statistical features, namely mean, standard deviation, kurtosis and skewness have been extracted from signals emitted from each electrode. A total of twenty features extracted from five-channel EEG data have been used as input in training against the output labels through four different machine learning (ML) classifiers such as random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), extreme gradient boosting algorithm (XGBoost). Same images have been used for training, and inferenced through a CNN based binary classifier. Results show a lower performance metrics with CNN than EEG feature-based classifiers. Such concept can overcome the high data related constraint in areas like manufacturing. © 2024 IEEE.

关键词： Image classification

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Machine Learning Solution for Police Functions

Machine Learning Solution for Police Functions

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Anandan, P. Kumar, A.M. Senthil Vignesh, C. Chandru Kokila, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vivekanandha College of Engineering for Women Namakkal India

ISBN: (纸本)9781665474511

In our society, crime has become one of the most prevalent problems. Preventing it is of paramount importance. In order to prevent it, crimes must be tracked and a public database must be maintained so that case histories can be retrieved when necessary in the future. This project is being proposed to reduce criminal activities by developing predictive models. From past experiments, many people have used deep learning methods to identify patterns of only one particular crime from past criminal activities that took place in different countries. In this research work, machine learning is being applied to predict the states which have high crime rates by analyzing the past crime data. Consequently, the police can focus on those areas where crimes are likely to occur. To predict this, machine learning algorithms like Decision trees and Random Forest algorithms were being used to classify and give accurate information. © 2023 IEEE.

关键词： Crime

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Prohibited Object Detection using YOLOv7 on X-Ray Images of Airport Luggages (OPIXray) 14

Prohibited Object Detection using YOLOv7 on X-Ray Images of ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Deena, S. Jayaprakash, L.S. Sudharson, S. Amrita Vishwa Vidyapeetham Computer Science and Engineering Amrita School of Computing Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350335095

Ensuring airport security and reducing risks for passengers safe travels is crucial. Conventional security inspection methods involve manual inspection of passengers luggage using X-ray scanners to identify any prohibited items. However, this manual process is prone to human error and can be time-consuming. To address these challenges, there is a need for automated security inspection systems that can enhance efficiency and accuracy. Deep learning techniques, particularly in the field of computer vision, offer potential solutions for autonomous security inspection. One such model is YOLO (You Only Look Once), a real-time object detection system capable of identifying multiple objects in images or video streams. This makes YOLO an ideal choice for airport security applications that require both speed and accuracy. This paper proposes a method for the automatic detection of prohibited objects in X-ray images, aiming to identify potential threats such as weapons, explosives, and other prohibited items in real-time. The proposed system was evaluated by conducting extensive tests on the publicly available benchmark dataset known as OPIXray. This dataset consists of labeled X-ray images that contain prohibited objects. The key component of our method involves the utilization of the YOLOv7 network, which was trained on a subset of the OPIXray dataset. The study demonstrates the effectiveness of this approach in detecting and recognizing prohibited items in OPIXray image datasets. The proposed methodology achieved a precision of 0.73, recall of 0.76, mAP@0.5 of 0.491, and mAP@0.5:0.9 of 0.397 on the OPIXray dataset. The experiments demonstrate the model's high accuracy and speed, thereby indicating its suitability for real-world airport security applications. Comparisons with other methods show that our model outperforms in terms of accuracy and speed. This paper presents a high-accuracy method for real-time airport security applications utilizing the YOLOv7 network. © 2023

关键词： Airport security

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Time Series Analysis of National Stock Exchange: A Multivariate Data science Approach 11th

Time Series Analysis of National Stock Exchange: A Multivari...

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11th International Conference on Soft Computing for Problem Solving, SocProS 2022

作者： Venkata Manish Reddy, G. Iswarya Kumar, Jitendra Choubey, Dilip Kumar School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bihar Bhagalpur India

ISBN: (纸本)9789811965241

Stock price prediction attracts individual decisions to invest in share market and may encourage the common people to become active in share trading. Stock trading gives us direct monetary benefit under prevalent uncertainties. Factors like political developments at state, national and international levels, conservative, diverse and complex social conditions, natural disasters, famine, pandemics, economic trade cycle (recession, boom and recursion) and many others have great effects on share prices and stock cost. The present work is based on secondary data sources acquisitioned from various public and private data portals accessed freely. There have been various efforts made in the past to predict the trends of the stock prices on the basis of secondary data. The predicted confidence estimates may enable the common investors to make a profit despite large risk of loss at different point of time under the dynamics of market fluctuations. The present work is based on an inferential methodology, which has been found to be instrumental and particularly suitable to the financial time series analysis. Time series modelling and forecasting have fundamental importance in stock market prediction and analysis. Further, with the application of multivariate methods and regression modelling, we expect better forecast accuracy. The implementation of the proposed approach has been incorporated on real-time data set on daily basis. The software used for various computations is Python, SPSS, MS Excel and MS Solver. The current work is relevant in prediction and analysis of the stock market prices and also seems to be useful to a large number of peoples engaged in day to day trading. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Commerce

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An Intelligent System for Preventing Accidents Due to Driver Distractions

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Procedia computer science 2024年 235卷 2196-2204页

作者： Giridhar S Rhithik Raj K Sreeram K P Nithish Menon Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The paper presented introduces an innovative accident prevention system that employs deep learning models and sensors to identify and alert drivers who lose concentration while driving for a variety of reasons. The system targets drowsiness, cell phone use, and alcohol consumption, which are among the leading causes of automobile accidents. The proposed solution employs neural networks to identify specific patterns associated with driver inattention and generates an audible alert to refocus the driver’s attention on the road. In addition, the system utilizes alcohol-detection sensors, as alcohol consumption is another significant cause of accidents. This system seeks to reduce the number of casualties by enhancing road safety considering the high number of daily collisions, which are primarily caused by speeding. The paper describes the design, architecture, and implementation of the system on a modest scale to provide a lifesaving, cost-effective solution.

关键词： Yolov8 CNN Drowsiness Sensors Arduino Alcohol

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