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Quantum Web-Based Health Analytics System 7th

Quantum Web-Based Health Analytics System

引用

7th International Conference on data Management, Analytics and Innovation, ICDMAI 2023

作者： Kumar, K. Pradheep Dhinakaran, K. Department of Computer Science and Engineering BITS Pilani Tamil Nadu Pilani100190 India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Tamil Nadu Chennai10587 India

ISBN: (纸本)9789819914135

In this work, a quantum web-based health analytics system has been proposed. The system uses a neuro-fuzzy algorithm which uses a selective rule-based strategy to process datasets. When a query is being raised, the datasets required are identified and processed to formulate inference for a knowledge repository. The knowledge repository is decentralized and ensures authentic access for a particular query. The quantum block chain algorithm reduces processing time and memory consumed by 24 and 28%, compared to the conventional block chain approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy sets

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IoT Smart City Air Quality Monitoring System Using Arduino

IoT Smart City Air Quality Monitoring System Using Arduino

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Gopinath, N. Chinnasamy, A. Sachin, K. Sakthiraj, S. Ravindharan, S. Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srmist Kattankulathur Campus Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350394580

Many factors, including population growth, increased vehicle use, industrialization, and urbanisation, have contributed to an increase in pollution levels throughout time, which has a negative impact on human wellbeing by adversely affecting the health of those exposed to it. To keep an eye on In this project, we'll build an IOT-based air pollution monitoring system that will track the air quality via an internet-connected web server and sound an alarm whenit drops below a certain threshold, which occurs when harmful gases like CO2, smoke, alcohol, benzene, and NH3 are present in sufficient quantities. On the LCD and on the website, the air quality will be displayed in PPM for easy monitoring. You can use your mobiledevice to check the pollution level as part of this IOT project from anywhere. © 2023 IEEE.

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Hybrid grasshopper optimization algorithm with simulated annealing for feature selection using high dimensional dataset 1

Hybrid grasshopper optimization algorithm with simulated ann...

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sahu, Bibhuprasad Ravindra, J.V.R. Mohanty, Sachi Nandan Panigrahi, Amrutanshu Department of Artificial Intelligence and Data Science Hyderabad India Department of Electronics & Communication Engineering Hyderabad India VIT-AP Andhra Pradesh Amaravati India Department of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665472753

In the era of machine learning, microarray data play a crucial role in identifying cancer diseases. The impact of redundant and noisy features degrades the learning model's performance. It may also increase the computational cost. The curse of dimensionality is the major concern in the case of microarray datasets. To eliminate this issue, feature selection methods play an effective role. This study proposes a hybrid filter-wrapper feature selection model using mRMR-Plus as a filter and grasshopper optimization algorithm as a wrapper. In the first stage of the proposed model, a ranked base filter mRMR-Plus is used to identify the top-ranked features from the original dataset. Cross-operator embedded simulated annealing (SA) is adopted to basic grasshopper optimization to develop a new wrapper model. The proposed model was tested with different cancer datasets to recognize the best optimal features. The result of mRMR-Plus-GO-SA is compared with different existing approaches. From the result and the comparative study, it's noteworthy to state that the new mRMR-Plus-GO-SA filter wrapper model performs far better as compared to its counterparts in terms of the number of features selected and accuracy. © 2023 IEEE.

关键词： Simulated annealing

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Enhancing Road Safety: An Intelligent Drowsiness Detection System Based on Deep Neural Networks 7

Enhancing Road Safety: An Intelligent Drowsiness Detection S...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nalavade, Jagannath E. Sachdeo, Rajneeshkaur Kale, Dattatray R. Buchade, Amar Subhedar, Mansi Shinde, Swapnil K. Mit Art Design and Technology University Computer Science and Engineering Pune India BRACT's Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India Pillai Hoc College of Engineering and Technology Electronics and Computer Science Rasayani India

ISBN: (纸本)9798331527822

The aim of this research is to create an intelligent vehicle system that can effectively mitigate the risks associated with drowsy driving, thereby preventing road accidents. Various factors like driver exhaustion, challenging road conditions, and adverse weather contribute to motor vehicle crashes, leading to over 1.35 million deaths annually, as estimated by entities such as the World Health Organization (WHO). To achieve this, the development of a robust warning system is crucial. In this study, we proposed an alert system to detect drowsy drivers. It utilizes a video stream process (VSP) to analyze the gaze's eye-aspect ratio (EAR) and geometric distance through an innate reflex model. Additionally, a facial landmark-based algorithm is employed for accurate eye recognition. When driver drowsiness is detected, the system issues a warning message, along with crash impact and location information, and activates the voice surveillance system. The suggested technique establishes a non-invasive approach for evaluating driver weariness in connection with accident severity. The camera, the fundamental element of this system, continuously acquires facial landmarks recognized by designated locations and analyzes them. Drivers who are distracted and have poor lighting may show signs of sleepiness. © 2024 IEEE.

关键词： Deep neural networks

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A Novel Hybrid Backscatter Assisted Cognitive Radio Networks for Maximum Throughput 9

A Novel Hybrid Backscatter Assisted Cognitive Radio Networks...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Pradeep, G. Srividhya, G. Therasa, P.R. Shanthy, K.R. Prabha, B. Vishnu Elangovan, K. Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India R M K Engineering College Department Computer Science and Engineering Tamil Nadu India Loyola Institute of Technology Department Computer Science and Engineering Tamil Nadu India Jeppiaar Institute of Technology Department Artificial Intelligence and Data Science Engineering Tamil Nadu India Siddharth Institute of Engineering and Technology Department Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The research work investigates a wirelessly powered cognitive wireless communication network (CWPCN) for Internet of Things (IoT) applications that consists of a main and secondary communication system. HTT and Backscatter communication (BackCom) are two innovative methods that we propose for the secondary communications system. Cognitive users have two alternatives when a main channel is overcrowded and the information receiver is unable to receive information: backscattering or collecting energy for future information transfers. Both are possibilities. To transfer information when the main channel is inactive, CUs employ bistatic scatter (BS) and high-frequency transmission (HTT) modes. To maximize secondary communication system throughput, time allocation between AB and energy harvesting and BS and HTT is examined. We find the best closed-form solution and operating modes for a particular CU circumstance. Numerical statistics show that our hybrid HTT/BackCom mode outperforms the benchmark mode. © 2023 IEEE.

关键词： Internet of things

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A Real-Time Video Surveillance-Based Framework for Early Plant Disease Detection Using Jetson TX1 and Novel LeafNet-104 Algorithm 4th

A Real-Time Video Surveillance-Based Framework for Early Pla...

引用

4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Dhanashekar, K. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India The Ohio State University Columbus United States

ISBN: (纸本)9789819726134

The precise and timely identification of plant diseases plays a pivotal role in safeguarding crop health and optimizing agricultural yields. This paper introduces an innovative framework designed for plant disease detection using video surveillance-based cameras strategically deployed on the Nvidia Jetson TX1 hardware platform within agricultural farms. Leveraging the capabilities of deep learning and the novel neural network architecture known as LeafNet-104, this framework empowers real-time disease detection on plants. Through the deployment of video surveillance cameras, continuous monitoring and early disease detection become achievable, enabling farmers to take prompt corrective measures and mitigate further crop damage. The Nvidia Jetson TX1, with its robust high-performance computing capabilities, enables on-device processing, rendering it ideal for real-time applications. To assess the efficacy of the proposed framework, comprehensive experiments were conducted on an actual agricultural farm. The dataset encompassed various plant species and disease types. Evaluation metrics such as accuracy, precision, recall, and the F1-score were employed to gauge the performance of the LeafNet-104 algorithm. The experimental findings underscored the framework's capacity to accurately and swiftly detect and classify plant diseases in real time, attaining an impressive accuracy rate of 97.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Enhanced Stain Normalization Method for Accurate Mitosis Detection in Histopathological Images 9

Enhanced Stain Normalization Method for Accurate Mitosis Det...

引用

9th International Conference on computer Science and engineering, UBMK 2024

作者： Samet, Refik Nemati, Nooshin Hançer, Emrah Sak, Serpil Kirmizi, Bilge Ayca Ankara University Department of Computer Engineering Ankara Turkey Victoria University of Wellington Center for Data Science and Artificial Intelligence Wellington New Zealand Ankara University Department of Medical Pathology Ankara Turkey

ISBN: (纸本)9798350365887

Mitosis on H&E-stained (Hematoxylin and Eosin) images is an important prognostic marker for evaluating the tumor's aggressiveness and providing comprehensive and reliable information for accurate diagnosis and treatment. The variability in dosage rate and different fading rates in H&E stains lead to challenges in histopathological analysis. Standardizing the staining process and understanding the factors that influence stain stability is critical for consistency and accuracy in diagnosis. This paper proposes an improved stain normalization method with DDPM-based (Denoising Diffusion Probabilistic Models) diffusion model to detect mitosis with high accuracy in histopathological images. One of the challenges in the DDPM-based diffusion model is incorrect transfer, where the model may confuse hematoxylin with eosin due to the high diversity nature of DDPM-based diffusion models. A stain separation method using CD-SNMF (Color Deconvolution-Sparse Non- Negative Matrix Factorization) is proposed to address this issue. This method normalizes the mitosis in images in CCMCT (Canine Mast Cell Tumors) and MIDOG21 (Mitosis Domain Generalization Challenge 2021) datasets by this stain normalization pathway with decent performance. © 2024 IEEE.

关键词： Non-negative matrix factorization

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FastPlane: A Fully Convolutional Network for Real-time 3D Plane Segmentation

FastPlane: A Fully Convolutional Network for Real-time 3D Pl...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zhang, Haijun Ren, Qingji Dong, Li Tsang, Kim Fung Harbin Institute of Technology Shenzhen China Department of Computer Science Shenzhen China Shenzhen Technology University The College of Big Data and Internet Shenzhen China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China

ISBN: (纸本)9798350324136

3D plane segmentation is a challenging computer vision task that involves the detection of planar regions in a scene and the prediction of their spatial position. Recent methods have addressed this problem as a multi-task learning problem in which convolutional neural networks with separate branches predict the plane segmentation mask and the dense depth maps, or plane parameters. However, existing methods often rely on a multi-stage object detection framework for plane segmentation, resulting in slow inference speeds that hinder real-time applications. To address this issue, we propose FastPlane, a real-time 3D plane segmentation network based on a fully convolutional structure. FastPlane comprises a shared backbone, a fully convolutional plane segmentation network, and a lightweight depth estimation network. Given a single image as input, FastPlane outputs both the plane segmentation mask and dense depth maps. Extensive experiments on public datasets demonstrate the effectiveness and efficiency of FastPlane, outperforming existing methods in terms of plane segmentation and depth estimation metrics. Notably, FastPlane achieves an impressive inference speed of 45.0 FPS, making it suitable for real-time applications in robotics, augmented reality, and virtual reality. © 2024 IEEE.

关键词： 3D plane segmentation depth estimation multitask learning scene reconstructing

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Application of data Analysis and Visualization Tools for U.S. Renewable Solar Energy Generation, Its Sustainability Benefits, and Teaching In engineering Curriculum

Application of Data Analysis and Visualization Tools for U.S...

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2024 ASEE Annual Conference and Exposition

作者： Radhakrishnan, Ben D. Altamirano, Nelson Jaurez, James J. Department of Engineering Data and Computer Sciences College of Business Engineering and Technology National University San DiegoCA United States Department of Finance Economics Marketing and Accounting College of Business Engineering and Technology National University San DiegoCA United States Department of Computer Science & Cyber Security College of Business Engineering and Technology National University San DiegoCA United States

The advancement of data Analysis technologies with visualization has gained significant ground in the industries and they are also gaining ground in higher education curriculum. This research will focus on the application of these techniques to the energy industry - in particular, solar renewable energy generation in all the States in the United States (US). One strong opposing action against the progression of climate change is the use of renewable energy. The objective of the research is to develop a case study on renewable solar energy and its impact on abating or preventing CO2 emissions to help reduce the severe impacts of climate change in the United States and demonstrate how it supports the 3Es of sustainability at the same time. This research paper will specifically explore the past production of solar energy in all the states in the US, and with the use of data analysis tools will predict the production to the year 2030. The reduction of CO2 emissions with the use of renewable solar energy is in direct support of the three elements of sustainability, namely the 3Es: Environment, Economics, and Equity (or social justice). This research will quantify the past benefits already realized in all these three areas for solar energy, and project them up to 2030. Cluster analysis technique will be applied to solar generation across all US States to identify group(s) at distinct levels of production. This can help States to follow the leading State(s) policy and process to increase their solar generation and thus help manage climate change. Solar energy generation challenges and recycling issues of solar equipment will also be addressed. This case study approach will fill a gap that currently exists in engineering education when it comes to exploring renewable energy and its sustainability benefits with modern data analysis tools. This research will use publicly available data sources (e.g., NREL, EIA), ubiquitous Excel, and open-source data analytics and prediction

关键词： Curricula

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Yesankar, Prajyot Verma, Prateek Rewatkar, Rajendra Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350382693

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets. © 2024 IEEE.

关键词： Sales

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