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Investigating the Addictive behavior of Internet Surfing through Deep Analysis of Brainwaves 23

Investigating the Addictive behavior of Internet Surfing thr...

引用

5th International Conference on Image, Video and Signal Processing, IVSP 2023

作者： Swati, Shipra Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9781450398381

Internet has become one of the essential part of human life in this current era. Apart from delivering extraordinary services to mankind, it provides livelihood to many through online survival opportunities. However, easy accessibility to the internet also resulted in major consequences in the form of uncontrolled nonwork internet access, called internet addiction (IA). The recent span of COVID pandemic boosted it to another level. It affects the cognitive strength of humans in various ways and reduces the quality of life. The presented work addresses these burning issues and an effective methodology is suggested for early identification of this addictive behavior by applying deep learning techniques to brain waves. The experimental result achieves an accuracy of 98%, which shows improvement over the state-of-the-art approaches without complex preprocessing and feature extraction techniques. This increases the feasibility of deploying the proposed method over IoT-supported infrastructure. This framework may also be applied to identify other cognitive conditions at an early stage for timely diagnosis and prognosis. © 2023 ACM.

关键词： Deep learning

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Review of Task Scheduling based on Different Parameters in Cloud Environments

Review of Task Scheduling based on Different Parameters in C...

引用

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

作者： Pradeep, K. Sharma, Kshitij Edara, Jishnu Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474511

In IoT applications where connectivity to cloud-based systems is irregular yet low latency is a crucial requirement, fog computing is necessary for success. Fog Computing's dispersed functionality enables storage and execution to take place in totally separate places. Resource management and multiple programming paradigms are required due to the distributed capabilities and numerous user applications taken together. It is important to manage and evaluate failures in network-based systems and services caused by a variety of limitations. The study's main goal is to shed light on various QoS-based scheduling strategies that aim to improve execution in fog computing to evaluate several fault-tolerant methods used in the fog computing environment. © 2023 IEEE.

关键词： Fog computing

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Identifying and Counting of Blood Cells Automatically using Machine Learning and Deep Learning Techniques 14

Identifying and Counting of Blood Cells Automatically using ...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Jogi, Shwetha Shilpa, B.K. Samyaktha, G.A. Akshatha, Y. Poornima, A.S. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

One of the important medical tests to evaluate overall human health condition is the Complete Blood Cell Count (CBC). Traditionally these blood cells were counted manually by visual inspection or by using the haemocytometer along with some chemical compounds and other scientific equipment’s which is a tedious task and more time consuming. To avoid this problem, the proposed work here is the machine learning approach of automatic identification and counting of blood cells (RBC’s, WBC’s and Platelets) using ‘You Only Look Once’ in short YOLO object detection and classification algorithm to automatically identify and count the blood cells from the blood smear images. This YOLO framework will be trained the modified Blood Cell Count Data set (BCCD) of blood smear images to automatically identify and count the RBC’s, WBC’s and Platelets from the blood smear images. Thus, overall, the computer aided system of counting and detection enables to count the blood cells just in less than a second and this can be useful in the practical applications. We choose YOLO framework which is about three times faster than Faster RCNN with VGG-16 architecture. YOLO uses a single neural network to predict bounding boxes and class probabilities directly from the full image in one evaluation. We retrain YOLO framework to automatically identify and count RBCs, WBCs, and platelets from blood smear images. In order to improve the counting accuracy, a verification method has been developed to avoid repeated counting by the framework. © Grenze Scientific Society, 2023.

关键词： Cells

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Prediction of Covid-19 Recovery Chances Using Supervised Machine Learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. The suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. Thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Research on Motion Image Processing System Based on FPGA and Convolutional Neural Network 7

Research on Motion Image Processing System Based on FPGA and...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Nie, Xin Yu, Chuan-Yi Chen, Yuan Wuhan Institute of Technology School of Computer Science & Engineering Wuhan China

ISBN: (纸本)9798350359145

Aiming at the problem of large amount of moving image data and difficulty in transmission, a moving image processing system based on FPGA and neural network is proposed. The hardware composition of the system is studied and the key modules such as FPGA module and storage module in the system are analyzed and designed, and an FPGA-based neural network realization method is proposed. Experimental results show that this method can achieve reliable compression of moving images. The compression effect is related to the number of neurons in the hidden layer. The more the number of neurons, the higher the signal-to-noise ratio of the compressed image. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Building ML Workflow for Malware Images Classification using Machine Learning Services in Leading Cloud Platforms

Building ML Workflow for Malware Images Classification using...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Jagati, Anika Subbulakshmi, T. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350323917

To meet the growing computational needs of machine learning, cloud services have provided a cost-effective alternative to fulfil the memory intensive task requirements. Cloud providers offer many benefits including more storage capacity, server access and high-end analytical capabilities. The abundant presence of various cloud providers makes it a tough choice for users and companies to select the best service provider to meet their needs. This paper aims to review and evaluate the top three cloud platforms;Amazon Web services, Google Cloud Platform and Microsoft Azure with respect to the various features and services provided by them by building a machine learning workflow on the platforms for an image classification task. © 2023 IEEE.

关键词： Machine learning

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Deep Learning Based Intelligent Waste Identification System 1

Deep Learning Based Intelligent Waste Identification System

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1st International Conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Nikhil, K.R. Aryan Chopra, Yash Ramasubramanya, M.S. Karthik, S. Department of Computer Science & Engineering BNM Institute of Technology Bangalore India

ISBN: (纸本)9781643683607

Garbage disposal and management have been one of the pertinent issues in every part of the world. Keeping the city clean is the collective responsibility of every citizen. Instead of blaming civic bodies for being waste vigilant, citizens should work with civic bodies to locate and report garbage in a visually informative and effective manner. In order to facilitate this, a novel CNN based garbage management system has been proposed in this work. In addition, an Android application has been developed to help citizens identify, locate, and report litter spots to maintain cleanliness. The user captures the image of the garbage pile. The CNN model identifies the garbage pile. If it is a garbage pile, the location is captured by the app, bundled with the image, and then uploaded into Google Sheets in JSON format via APIs. The app reads the JSON file and updates the location of litter across all the remaining users. The proposed model has obtained an accuracy of 91.23% in garbage identification. © 2023 The authors and IOS Press.

关键词： Piles

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An Intelligent Pothole Management System Using Multi-Tasking CNN on Android 20

An Intelligent Pothole Management System Using Multi-Tasking...

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20th IEEE India Council International Conference, INDICON 2023

作者： Chourasia, Srajan Kumar, Yohan Sinha, Sharad Indian Institute of Technology Goa Computer Science & Engineering Goa India

ISBN: (纸本)9798350305593

Potholes on the road can pose significant risks to drivers and pedestrians. They are a common cause of accidents and vehicle damage. Hence, their timely repair is critical for road safety. However, pothole repairs are frequently delayed due to lack of information about their location and risk level. To address this issue, we propose an Intelligent Pothole Management System (IPMS) that promotes using citizen-sourced information through an android application in a citizen-government collaborative environment. The android application employs a novel lightweight multitasking CNN to detect and classify potholes based on their risk level instead of traditional computer vision methods that rely on calculation of geometric dimensions. The proposed model runs on the smartphone, compared to a server environment, as it relies on perspective vision instead of exact calculations and is trained using a carefully curated dataset of about 30,000 images. Our system, similar to Google Maps' traffic view, shows citizens the pothole density as well as pothole severity on each road to avoid potential accidents. Citizens report potholes through the application which captures geo-location and extracts pothole information (risk level). Similarly, public works department as access to pothole relevant data in a dashboard, which they can use to prioritize repairs based on the risk levels of potholes and their location. By allowing citizens to report potholes directly, we hope to foster a sense of community ownership and responsibility for road safety. Our proposed system can also assist public works department in making more informed repair decisions, resulting in more efficient repairs and safer roads. We believe that our Intelligent Pothole Management System (IPMS) has the potential to improve road safety and is a valuable contribution to the development of intelligent transportation systems. © 2023 IEEE.

关键词： Android App Community Ownership Machine Learning Multitasking CNNs Pothole Management System

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Analysis of Deep Learning Based Garbage Detection in Water Bodies 5

Analysis of Deep Learning Based Garbage Detection in Water B...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Vijayanti, Veernala Kar, Akankshya Navya, K.N.S. Siva Sankari, S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350321425

The pollution of water bodies is an important problem for society. Harmful substances like nitrogen, lead, pesticides and oil spills affect aquatic life. The visualization of garbage gets difficult resulting in poor image resolution in water bodies. Object detection methodologies used in marine litter detection such as background subtraction and frame difference cannot distinguish the shape of garbage and background due to low resolution of images. The deep learning with transfer learning-based methodologies like ResNet50 and VGG19 handles the low-resolution images and performs effectively the garbage classification and detection. The main objective of the proposed research work is to contribute marine ecosystems maintenance by reviewing and analyzing the garbage detection and classification in water bodies. It reviews the significance of garbage detection in water bodies, open problems in garbage detection, role of deep learning in garbage detection, working of standard convolutional neural networks such as VGG19(Visual Geometry Group) and ResNet50 (Residual Network). The CNN, VGG19 and ResNet50 models are experimented with the AquaTrash dataset. Accuracy is utilized as the performance metric leveraged to represent performance of the methodologies. The experimental result shows that VGG achieved an improved garbage detection compared to others. © 2023 IEEE.

关键词： Convolutional neural networks

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A Hybrid Approach to Support the Detection of Autism Spectrum Disorder(ASD) through Machine Learning and Deep Learning Techniques 12

A Hybrid Approach to Support the Detection of Autism Spectru...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Manoj, Meghna Praveen, Joe I R Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350318210

Autism spectrum disorder is a significant contributor to developmental disabilities worldwide, and its estimated prevalence in India is more than 2 million people. Research has emphasized that early detection and treatment can help to improve a person with autism's level of language, communication, and overall well-being. The main objective is to automate the diagnosis process, compare and identify the best classifiers, and find out the most significant traits for supporting detection. We attempted to investigate the use of Logistic Regression, Decision Tree, Naive Bayes, Random Forest, Support Vector Machine, Linear Discriminant Analysis, K-Nearest Neighbors, XGBoost Classifier, Multi-Layer Perceptron for predicting if the person is susceptible to ASD. For the analysis, ASD data for toddlers, children, adolescents, and adults were employed, and the best classifier techniques were determined using measures like recall, precision, F-measures, etc. Feature engineering was performed to identify the most impactful and effective features of the datasets. We identified that LR and MLP models provide the best results in adult and toddler datasets. In case of adolescent data, LR classifier showed the highest performance whereas LR and XGBoost algorithms worked well for child dataset. The results obtained justify that ML and DL techniques showed promise in developing an improved system for supporting the detection of Autism. © 2023 IEEE.

关键词： Diseases

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