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Colon Cancer Prediction with Transfer Learning and K-Means Clustering 7th

Colon Cancer Prediction with Transfer Learning and K-Means...

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7th International Conference on Emerging applications of Information Technology, EAIT 2022

作者： Babu, Tina Nair, Rekha R. Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Karnataka Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

ISBN: (纸本)9789811951909

The automatic diagnosis of colon cancer is important for patients and their prognosis through the analysis of histopathological images. Traditional feature extraction methods extract low-level image features, and prior knowledge is required to choose meaningful features, which may be modified significantly by humans. Hence, unsupervised and supervised deep convolutions neural networks were utilized to analyze histopathological images of colon cancer. To overcome the impact of the unbalanced histopathology images in sub-classes, the balanced sub-classes are turned right and left, up and down, and rotated counter clockwise by 90∘ and 180∘. The proposed experimental findings for supervised histopathological image classification of colon cancer demonstrate that Inception_V3 and Inception ResNet_V2 outperform current algorithms. These findings suggest that the Inception_ResNet_V2 network is superior deep learning architecture for analyzing histopathological images for diagnosis colon cancers. As a result, in order to perform unsupervised image analysis, Inception_ResNet_V2 is utilized to extract features from colon cancer histopathology images. In addition, a new autoencoder network was created to convert the features collected by Inception_ResNet_V2 to a low-dimensional space for image clustering analysis. The test results demonstrate that the proposed autoencoder network outperforms the Inception_ResNet_V2 network in terms of clustering. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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A Hybrid Watermarking Scheme Using DWT and Haar Transform for Image Authentication 3rd

A Hybrid Watermarking Scheme Using DWT and Haar Transform fo...

引用

3rd International Conference on Advanced Computing and applications, ICACA 2024

作者： Jana, Sachchidananda Pal, Pabitra Mahadani, Asim Kumar Giri, Debasis Department of Computer Science & Applications Prabhat Kumar College Contai West Bengal Purba Medinipur721404 India Department of Computer Applications Maulana Abul Kalam Azad University of Technology West Bengal Nadia741249 India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering West Bengal Bankura India Department of Information Technology Maulana Abul Kalam Azad University of Technology West Bengal Nadia741249 India

ISBN: (纸本)9789819747986

The safe transfer and preservation of medical images is crucial in the age of digital healthcare. Watermarking medical images is an essential method of adding patient data, hospital logos, and other pertinent information to these images without compromising their confidentiality, integrity, or validity. To overcome the aforementioned difficulties, this abstract offers a unique and reliable medical image watermarking system that makes use of Particle Swarm Optimization (PSO), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) approaches. There are three main phases to the suggested plan. The medical image is first broken down into its frequency components using DWT, which makes watermark embedding more effective. Subsequently, the PSO method is utilized to maximize the choice of image segments for incorporation, guaranteeing that the watermark remains undetectable and robust against diverse attacks. In the last step, the watermark’s strength is increased by using the SVD approach to embed it in the singular values of certain image blocks. Comprehensive tests are conducted to assess the efficacy of the suggested approach, taking into account performance measures like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as resistance against typical assaults such as noise addition, compression, and cropping. The testing findings show that the method effectively embeds the watermark while maintaining image quality and making sure it is robust to attack. The suggested method is appropriate for clinical and telemedicine applications as it also protects the confidentiality and integrity of patient data. By advancing the development of effective and safe medical image watermarking methods, this research helps to protect the privacy and veracity of critical healthcare data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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Discovering the Performance of MANET with Malicious and Non-malicious Node Using Newton–Raphson Method

Discovering the Performance of MANET with Malicious and Non-...

引用

World Conference on Information Systems for Business Management, ISBM 2022

作者： Ganesan, A. Kompaiya, A. Kumar PG and Research Department of Computer Applications Hindusthan Arts College Coimbatore641 028 India Department of Computer Science Chikkanna Goverment Arts College Tirupur641 602 India

ISBN: (纸本)9789811974465

The behavior of malicious and non-malicious nodes in mobile adhoc networks (MANET) is examined in this study using the Newton–Raphson method. This research uses the Newton–Raphson method to analyze the behavior of mobile adhoc networks (MANET) with malicious and non-malicious nodes. To find the infiltration, this MANET performance data is helpful. The network simulator is used to assess MANET's performance. Network measures including energy consumption, packet delivery ratio, and delay are used in the Newton–Raphson approach to determine the performance of the network. Three alternative configurations of the Medium Access Control (MAC) protocol—low;medium, and high—were used to carry out the experiment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification

Industry 4.0 based Machine Learning Models for Anomalous Pro...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Kumar, Sourabh Chandra, Saroj Kumar Shukla, Ram Narayan Panigrahi, Lipismita Computer Science and Engineering Op Jindal University Center of Excellence in Industry 4.0 Raigarh India Kalinga Institute of Industrial Technology Department of Computer Applications Bhubaneswar India

ISBN: (纸本)9781665492942

Automation has made tremendous changes in the industries. It has been used to automate the manual processes involved in different physical units of the industries. The purpose was to increase the production in the manufacturing. Now, computers are being used in the industries to monitor functionalities of different production units with the help of artificial intelligence and internet of things (IoT). The IoT has revolutionized the industries. It is an interconnected network system of physical units. The core purpose of it to gather and share information among different physical units. The IoT has great impact on the many areas such as business, industry, medicine, the economy, transport, industrial robots and automation systems. IoT with artificial intelligence has wide range of industrial applications. Industry 4.0 is used in the industries where different industrial units are connected over the internet and interacting to make decisions via machine-to-machine communication. It has increased the benefits of industries in terms of production and supply chain management. Manufacturing industry monitors its production units in every 10 milliseconds to capture features of the product that is being produced. The features generated in this process are huge in amount. Critical observation is performed on the generated features to categorize the product as anomalous or good. Product classification is difficult task in the labeled datasets due to human bias in labeling the final product as anomalous or good. In this work, machine learning models is being used to detect and classify faulty product produced by manufacturing industry. Both qualitative and quantitative study will be carried out to compare various machine learning models. © 2023 IEEE.

关键词： Internet of things

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Personal Workout Trainer Using MediaPipe and OpenCV for Real-Time Guidance and Feedback

Personal Workout Trainer Using MediaPipe and OpenCV for Real...

引用

Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S.P. Priyanka Sophia Janit Department of Computer Applications Hindustan Institute Of Technology and Science

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Home workouts have gained significant popularity, but the lack of professional guidance often leads to improper exercise techniques, increasing the risk of injuries and reducing workout efficiency. To address this challenge, a virtual fitness tracker integrates cutting-edge technologies like OpenCV, MediaPipe, and Gemini LLMa to provide an intelligent and adaptive fitness solution. OpenCV processes video feeds to capture user movements accurately, while MediaPipe detects and tracks key body landmarks in real-time, ensuring precise pose estimation. By analyzing this data, Gemini LLM delivers personalized feedback, dynamically adjusting suggestions to correct user form and optimize performance. The system not only bridges the gap between professional trainers and home fitness but also enhances accessibility by offering an interactive and user-friendly experience. With capabilities like exercise repetition tracking, posture correction, and injury prevention, this solution transforms workouts into a safe, engaging, and results-driven experience. The approach demonstrates the potential of integrating computer vision and advanced language models to revolutionize personal fitness training.

关键词： Training computer vision Tracking Visual analytics Pose estimation Transforms Streaming media Real-time systems Injuries Standards

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Predicting Future Traffic Conditions in Urban Areas

Predicting Future Traffic Conditions in Urban Areas

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： V. Dharshini H.J. Shanthi Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Traffic jams are fast becoming a severe problem in urban areas, impacting day-to-day life, consumption of fuel, and the surrounding environment. The paper proposes an efficient and real-time traffic prediction system that analyzes traffic conditions using video streams. In our scheme, vehicles are detected and counted from live video using background registration and contour detection using OpenCV. The actual count of vehicles is obtained, and a Linear Regression model is trained to predict future traffic density. One can also integrate XGBOOST with it for better accuracy in prediction. The purpose of this work is to develop a solution that can be low-cost and scalable for use on existing surveillance infrastructure without additional hardware or sensors. By adding vehicle detection to a machine-learning-based prediction, the system can facilitate effective traffic control, signal optimization, and urban planning. Experimental results show that the model performs reliably in different scenarios, enhancing its chance for deployment in smart city traffic management systems.

关键词： Adaptation models Accuracy Linear regression Urban planning Streaming media Predictive models Traffic control Real-time systems Hardware Meteorology

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IoMT with Cloud-Based BC Disease Diagnosis Healthcare Framework Using Deep Convolutional Neural Network with Long Short-Term Memory 3

IoMT with Cloud-Based BC Disease Diagnosis Healthcare Framew...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Babu, C.Naveeth Kathiresan, V. Gomathi, R. Karthik, R. Priya, S.Sugantha Kumar, S.Senthil Department of Computer Science Bangalore560077 India Chinnakarai Tiruppur India Coimbatore641 035 India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore640142 India Dr Sns Rajalakshmi College of Arts and Science Department of Computer Science India Department of Information Technology Coimbatore India

ISBN: (纸本)9798350322842

Sensing devices on the human chest are linked through the Internet of Medical Things (IoMT) to remotely monitor health data on multiple dimensions, including physiological and behavioural impulses. IoMT devices in clinical use cloud computing to generate enormous volumes of data supplemented by portable equipment's insufficient memory and computational power. An IoMT-based healthcare diagnosis model will be developed using a deep learning methodology. It is the driving force behind the development of a new IoMT-based medical system for the early detection of breast cancer (BC) using a Deep Convolutional Neural Network with Long Short-Term Memory (DeepCNN-LSTM). The suggested DeepCNN-LSTM model yielded significant gains in accuracy, recall, F-measure, and recall, as demonstrated in the extensive experimental investigation. © 2023 IEEE.

关键词： Long short-term memory

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Mental Health Predictive Analysis using Multiple Machine Learning Techniques 2

Mental Health Predictive Analysis using Multiple Machine Lea...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier Logistic Regression Algorithm Naïve Bayes Random Forest Stacking

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The λNF Problem 2nd

The λNF Problem

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2nd International Conference on Cybersecurity and Evolutionary Data Engineering, ICCEDE 2022

作者： Dutta, Anurag Munjal, DeepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Computational complexity Dnf sort Partitioning Sorting algorithms

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Open Information Extraction System For Extracting Relations in Legal Documents 3

Open Information Extraction System For Extracting Relations ...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Raj, Nikhil Thomas, Shiyon Veena, G. Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Open Information Extraction system (OIE) extracts textual tuples consisting of arguments and its relation within the input sentence. OIE can also be considered as an unrestricted variant of information extraction. Throughout the years these types of systems have become more and more popular. The main factor for considering such a system is that they are made for speed, then any other factor. Also, one of the major advantages of using such a system is that the relations that are extracted are human-readable. Relation extraction in the legal domain has always remained a challenge because there is no structured data available. hence making this process more challenging. Since we are working in the legal domain, extracting the relations that are present in the document is very important. This leads us to a system that is capable of assisting people in the domain. The introduction of such a system would be really important for professionals like judges, advocates and also for the common people if they want to have a quick glance at certain documents. The outcome of these systems can be really helpful for future downstream applications like question answering, sentence similarity etc. © 2022 IEEE.

关键词： Information retrieval systems

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