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Fast Hybrid Adaboost Binary Classifier For Brain Tumor Classification

IOP Conference Series: Materials Science and engineering 2021年第1期1074卷

作者： S. Jayaprada G. JayaLakshmi L. KanyaKumari Associate Professor Department of Computer Science & Engineering Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Assistant Professor Department of Information Technology VR Siddhartha Engineering College Vijayawada Andhra Pradesh India Assistant Professor Department of Information Technology Andhra Loyola Institute of Engineering and Technology Vijayawada Andhra Pradesh India

The brain tumor is a dangerous disease and its characterization is a difficult undertaking for radiologists in light of the heterogeneous idea of the tumor cells. computer-Aided tasks and its implementation of the current models with their frameworks would suffice the design metrics to recognize and relate the different tumors including brain though the process with scanning of the brain would emphasize on MRI. The models with support vector machine-nearest neighbor, naïve bayes analysis are obtained aren't enough to produce the different scenario at each set of layers and its correlation values of the performance observed. We propose a design model with fast Adaboost binary classifier, ensemble approach with fast boosting algorithm with the pre-trained model's dataset to analyze the different problem which proposes a strategy for multiple application scenarios of feature extraction to provide a classification model for different tumors in the brain to improve different performance parametric values for each trained and test sets with the prediction algorithm with each design formulation.

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Compressing Biosignal for Diagnosing Chronic Diseases

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Journal of Physics: Conference Series 2021年第1期1998卷

作者： P. Ithayarani A. Jainul Fathima Associate Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation (KLEF) Deemed to be University Green Fields Vaddeswaram Guntur District A.P. India Assistant Professor Department of Information Technology Francis Xavier Engineering College Tirunelveli TN. India

Electrocardiogram, electromyogram, electroencephalogram are the foremost required vital signs for diagnosing chronic diseases like sleep disorder, mood disorder, epilepsy etc., which demands long-term monitoring. A sensor based wearable system which is enabled with internet technology, supports the continuous recordings of these vital signs without troubling the patient's daily activities. And the wearable hub is responsible for collecting the readings of bio signals from multiple micro-sensor nodes deployed around the body which creates the short range of communication and forward to the observer. These continuous monitoring increases the signal transmission cost and declines the battery life of wearables. So, the observed multiple bio signals can be compressed jointly than individually before sending, at an edge level. This paper proposes transfer learning based multimodal convolutional denoising autoencoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with better quality score than by randomly initialized weights. The proposed work achieves compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in case of consuming multiple sensors. And the experimental result proves that the computation cost is low in multimodal compression than in unimodal compression.

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LRFM model for customer purchase behaviour using K-Means algorithm

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： C Jamunadevi S Tamil Selvan M Govindarajan C Saravanan B R Janaki Raman Assistant Professor Department of CT-PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India Associate Professor Department of Computer Applications Velalar College of Engineering and Technology Erode India Assistant Professor Department of Information Technology Velalar College of Engineering and Technology Erode India UG Scholar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

The COVID-19 pandemics have a major collision on every aspect of life, including how people shop for their requirements. As the pandemic has reshaped life as we know, it's also initiated many trends – but the biggest of these trends may be online shopping. The shift toward online shopping was happening before the pandemic, but according to new statistics from IBM, the COIVD-19 has accelerated consumers shift toward online shopping by 5 years. The chief idea of the article is to inspect if the situation is approaching people to purchase things online and the continuation of shopping things online even after the end of pandemic. The information for the article has been gathered by circulating the survey on social networks. The questionnaire is comprised of 12 different questions, and 615 people responded to it. This work is based on LRFM (Length, Recency, Frequency, and Monetary) replica and separation of data based on the questionnaire using K-Means algorithm. Silhouette analysis helps to decide the extent of division among clusters. The results of the survey has a termination that people are fond of purchasing products online through the lockdown and people too agreed that the rate of online shopping will increase in the future when this pandemic is over.

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Efficient Feature Extraction from Multispectral Images for Face Recognition Applications: A Deep Learning Approach

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： R Sudharsanan PV Gopirajan K Suresh Kumar Assistant Professor Department of Computer Science and Engineering Karpaga Vinayaga College of Engineering and Technology GST road Padalam Chengalpattu Tamil Nadu - 603308 India Assistant Professor (SG) Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Associate Professor Department of Information Technology Saveetha Engineering College Tamil Nadu India

In recent years many face recognition algorithms were used for the identification and authentication of a person to a system. However, still, feature extraction from multispectral images was considered to be a challenging task. Feature extraction, including highlight location and portrayal, assumes a significant job in real-time security-based applications. In this paper, a novel Geometric Algebra-based Multivariate Regression Feature Extraction (GA-MVRFE) algorithm was proposed to extract features from a huge dataset stored in the cloud efficiently. This proposed algorithm works with the supreme expedient deep learning approach - Convolutional Neural Network (CNN) for image classification. CNN will automatically detect significant features from the multispectral images without any human intrusion from a huge database. Real-time images were captured with three different cameras and applied filters over the images and were created as a dataset. To show the competence of the proposed algorithm, an exclusively created dataset with a set of 14,400 image data was applied in the proposed and other existing algorithms, and their efficiency and robustness were noted. Providentially, GA-MVRFE produced better accuracy in 'Face Recognition' with a less time fraction compared with former algorithms. Obtained accuracy % for Geometric Algebra Oriented fast and Rotated Brief (GA-ORB), Geometric Algebra Fast Retina key-point Extraction Algorithm (GA-FREAK), Trilateral Smooth Filtering (TRSF), Cross Regression Multiple View Features extraction (CRMVF) and GA-MVRFE was 87.81, 83.23, 90.72, 91.67 and 97.57 respectively.

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An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT

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IOP Conference Series: Materials Science and engineering 2021年第1期1131卷

作者： Sanwta Ram Dogiwal Pankaj Dadheech Ankit Kumar Linesh Raja Abhishek Kumar Mahender Kumar Beniwal Associate Professor Department of Information Technology Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Associate Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Applications Manipal University Jaipur Rajasthan India Assistant Professor Department of Computer Science & IT Jain (Deemed to be University) Bangalore India

AI Solution for Farmers is an agricultural based project, created to help farmers and to help them in increasing their productivity. Since the technologies are running the world, why agriculture must be deprived of it. Agriculture is one of the most important areas that have major impacts on the economy and society of a country. Technological developments serve as tools to share knowledge and practices of agricultural products and make more satisfactory lives for farmers, traders, policymakers, and the overall society. It is evident that knowledge has become a crucial component in production, society, food security, health, poverty, and other millennium development goals. The use of technology may provide a better approach to solve the problems arising from sowing the seed to harvest the crop. The new technologies like Machine Learning and Data Science may be great help to have an eye on the deciding factors to the growth of crop.

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Join the Fold: Video Games, Science Fiction, and the Refolding of Citizen Science

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DESIGN ISSUES 2023年第1期39卷 70-87页

作者： Milburn, Colin Buse, Katherine Dhaliwal, Ranjodh Singh Wills, Melissa Aldosari, Raida Camarador, Patrick Miller, Josh Aaron Siegel, Justin Colin Milburn is Gary Snyder Chair in Science and the Humanities and a professor of Science and Technology Studies English and Cinema and Digital Media at the University of California Davis. His research focuses on science literature and media technologies. His books include Nanovision: Engineering the Future (2008) Mondo Nano: Fun and Games in the World of Digital Matter (2015) and Respawn: Gamers Hackers and Technogenic Life (2015). Katherine Buse is an assistant professor of Media Studies at the University of Chicago. Her research focuses on digital media the environmental humanities and the cultures of science and technology. Her current book project Speculative Planetology: Science Culture and the Building of Model Worlds tells the story of how the sciences and the arts co-constructed a toolkit for thinking about planets as whole systems. Ranjodh Singh Dhaliwal is Ruth and Paul Idzik College Chair in Digital Scholarship and an assistant professor of English and Film Television and Theatre at the University of Notre Dame. His work spans media theory science and technology studies and literary criticism. His current book project Rendering: A Political Diagrammatology of Computation shows how our cultural politico-economic and epistemic formulations crystallize into hardware and software architectures. Melissa Wills is a visiting scholar in the Department of Science and Technology Studies at the University of California Davis. She completed her PhD in English at UC Davis in 2021. Her research addresses the relationship between the life sciences and science fiction. Her current book project Ruptures: Life without Germs in the Microbiome Era examines microbiology's debt to the imagination of germ-free worlds. Raida Aldosari is the lead artist on the Foldit: First Contact development team. She received her BS in Nutrition Science from the University of California Davis in 2018. She has experience as a freelance graphic designer and as a project manager for nutrition training and courses.

This article explores the value of science fiction narratives in games for citizen science. Focusing on the protein-folding game Foldit, it describes the process of modifying and redesigning the game to feature a framing narrative and other alterations to the main tutorial campaign. The campaign narrative, Foldit: First Contact, situates the practices of citizen science in an expanded context of meanings and ethical implications, promoting critical self-reflection on the relations of science and civic values. A study of player responses to Foldit: First Contact suggests the significance of science fiction and critical game design for attuning citizen scientists to the collective responsibilities of experimentation and innovation, drawing attention to the intersecting social, technical, and environmental domains in which gamers may contribute to scientific research.

关键词： citizen science science fiction video games narratives

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A Comparative Study: Performance and Optimization of Artificial Neural Networks in Blood Cell Datasets

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Journal of Physics: Conference Series 2021年第1期2040卷

作者： K. Sivaraman V. Khanna Research Scholar Department of Computer Science and Engineering Department of Information Technology Bharath Institute of Higher Education and Research (BIHER) Chennai-73 Tamil Nadu India. Professor Department of Computer Science and Engineering Department of Computer Science and Engineering Department of Information Technology Bharath Institute of Higher Education and Research (BIHER) Chennai-73 Tamil Nadu India.

Artificial Neural Networks (ANN) generally referred to as Neural Networks are the statistical model, It is mainly focused on the biological neuron for the information or signal processing. A neural network is a dynamic system consisting of a community of interconnected neurons that offers a very exciting solution to complex problem solving and other applications that can play a crucial role in today's technology. In this paper we are using various process like pattern recognition, prediction and optimization of blood cells is usually undertaken to help clinicians determine a wide variety of haematics. Pathologies identify dengue, anemia, diabetes and other related diseases.

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Contactless Attendance Management System using Artificial Intelligence

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Journal of Physics: Conference Series 2021年第1期1714卷

作者： M Rajamanogaran S Subha S Baghavathi Priya Jeevitha Sivasamy Data Science Consultant Chennai Tamil Nadu India Associate Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Professor Department of Information Technology Rajalakshmi Engineering College Chennai Tamil Nadu India Research Scholar Department of Computer and Information Science Annamalai University Chidambaram Tamil Nadu India

Attendance monitoring is one of the vital administrative processes in all educational institutions and organizations. A well-structured system will enable the institutions to have an increasing growth. It helps the students and teachers in all ways to have a good progress in the attendance, thereby reducing the teachers' time and effort. In physical classrooms, traditional method of calling the students names and marking their presence/absence is the routine process followed regularly. Attendance monitoring system is proposed using artificial intelligence. Firstly, a database containing the facial images of the students in a particular class is constructed. Knowledge gained using Convolutional Neural Network (CNN) is reused in a perfect manner using transfer learning. This system is designed to improve the students' engagement time inside the classroom, to communicate to the parents frequently, to avoid proxy attendance and to generate detailed reports for future reference.

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Real Time Emotion Analysis Usingbrainwavesin Comparison to Facial Expression

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IOP Conference Series: Materials Science and engineering 2020年第1期994卷

作者： S. Devi Dr. M. Rajalakshmi S. Saranya B. Jeevanandan A. Ramya Assistant Professor Department of Information Technology Associate Professor Department of Information Technology Assistant Professor Department of Computer Science and Engineering B. E Student Department of Information Technology Coimbatore Institute of Technology

Emotions play a vital role in human living and are correlated with every single word which is being spoken, without them life will be saturated and irrelevant. Understanding of the real apparent facial expressions might be difficult because part of the emotion might not be revealed outside by everyone. In the proposed method, the emotions of the human are analyzed in two different ways so that the real hidden emotion of the subject can be detected. Every human has unique brain waves, which gives better accuracy than other techniques. It ensures that the facial expression exhibited outside is real or not. The emotion analyzed will be useful in treating the person with high mental stress and also infinding the emotion of differently abled person who requires medical treatment.

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Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Vinay Kumar Baraa Riyadh Altahan Tariq Rasheed Prabhdeep Singh Devpriya Soni Hashem O. Alsaab Fardin Ahmadi Department of Computer Science Dyal Singh Evening College (University of Delhi) Delhi 110003 India Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University College Hilla Babylon Iraquomus.edu.iq Department of English College of Science and Humanities Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabiapsau.edu.sa Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun 248002 Uttarakhand Indiageu.ac.in Associate professor Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida Indiajiit.ac.in Department of Pharmaceutics and Pharmaceutical Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabiatu.edu.sa Addiction and Neuroscience Research Unit Taif University Taif 21944 Saudi Arabiatu.edu.sa Computer Science Faculty University Rana University Kabul Afghanistan

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians’ detection rate was 74.60 percent lower than deep learning’s detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.

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