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Breast Cancer Detection using Deep Learning

Breast Cancer Detection using Deep Learning

引用

IoT, Communication and Automation technology (ICICAT), International Conference on

作者： Shajal Afaq Nanhay Singh Department of Computer Science and Engineering Netaji Subhas Institute of Technology New Delhi

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Breast cancer is a leading cause of morbidity for women all over the world, BC, or breast cancer, affects many women globally. It ranks fourth in fatality among colon, brain, and cervical malignancies. New BC cases are predicted to rise 70% in the following two decades. Thus, early identification and accurate breast cancer diagnosis improve prognosis and long-term survival. Machine learning methods like "Support Vector Machines" (SVM) and Nearest Neighbor have shown promising but limited efficacy in recognizing BC. This study introduced a "Convolutional Neural Network" (CNN) model of utilizing deep neural networks to diagnose BC using the Wisconsin BC database. The research demonstrates the application of DL technology for diagnosing BC using the Kaggle Dataset. The research method involves data acquisition, data preprocessing and using the dataset to train the CNN model and assess its performance using four key metrics: precision, recall, accuracy, and F-measure. CNN model accuracy is 95.57%, precision is 97.70%, recall is 98.64%, and F-measure is 98.14%. This approach outperforms standard models, proving its effectiveness in detecting BC.

关键词： Support vector machines Deep learning Measurement Accuracy Databases Data preprocessing Breast cancer Convolutional neural networks Prognostics and health management Standards

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Working from Home in the Post-Pandemic World: A Structural Equation Modeling-based Study

Working from Home in the Post-Pandemic World: A Structural E...

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Chakraborty, Pinaki Mittal, Prabhat Kanika Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India Department of Commerce Delhi India Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350396485

The COVID-19 pandemic forced organizations and employees to switch to working from home. We conducted a survey to understand the opinion of people on working from home, and if they would like working from home after the pandemic is over. We found that the respondents, most (92%) of whom were in IT-related professions, appreciated the comfort and flexibility afforded by working from home, but also had difficulties because of lack of facilities at home, low motivation and reduced synchronous communication, collaboration and teamwork. The possibility of IT-related activities (t= 2.214), flexibility (t= 2.603) and effective communication (t=2.012) made the respondents feel that working from home can lead to positive changes in society (P © 2023 IEEE.

关键词： COVID-19

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Comparative Analysis for Speech Recognition Using DeepSpeech

Comparative Analysis for Speech Recognition Using DeepSpeech

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Chanpreet Singh Aryan Singh Himanshu Upreti Shobha Bhatt Kirti Sharma Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science Engineering Netaji Subhas University of Technology Delhi India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The presented work aims to implement speech recognition systems for Hindi and English using advanced deep learning. Enhancing ASR technologies is crucial for improving digital communication's accessibility and efficiency across different linguistic contexts, which is pivotal for fostering broader adoption of technology in multilingual environments. The experiments were conducted using speech corpora such as LJSpeech(English Language) and OpenSLR(Hindi Language). The novelty of this research lies in the tailored adaptation of hybrid deep learning architectures, combining the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for sequence learning, specifically configured for the acoustic properties of English and Hindi. These architectures are integrated using connectionist temporal classification to enhance transcription precision without the need for linguistic pre-segmentation. The research findings provide evidence that the implemented hybrid deep learning architectures significantly enhance the performance of ASR systems. By specifically addressing the unique challenges posed by Hindi languages, the models demonstrate improved accuracy in transcription, particularly in environments with varied acoustic properties. The system obtained a minimum WER of 21.89. This approach showcases how advanced neural network configurations can adapt to complex linguistic features, thus reducing the Word Error Rate and improving the overall reliability of speech recognition systems in real-world applications.

关键词： Deep learning Adaptation models Accuracy Recurrent neural networks Instruments Speech recognition computer architecture Linguistics Reliability engineering Acoustics

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An Experimental Investigation of the Effect of Diethyl Ether as an Additive on the Performance of a Single Cylinder Diesel Engine

An Experimental Investigation of the Effect of Diethyl Ether...

引用

International Conference on Mechanical engineering, INCOME 2021

作者： Ahmad, Aqueel Singh, Achhaibar Singh, D.K. Yadav, Ashok Kumar Mechanical Engineering Department Netaji Subhas University of Technology New Delhi India Department of Mechanical Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (纸本)9789811934971

The use of diethyl ether (DEE) as an oxygenated additive in a diesel engine is studied in this experiment. The effect of blending diethyl ether (DEE) with diesel at 6, 10, and 14% on the performance analysis of a single cylinder diesel engine running at a constant speed of 1500 rpm is proposed in this research. The results showed that adding DEE enhances brake thermal efficiency while significantly reducing brake specific fuel consumption and fuel flow rate. With a 14% blending of DEE with diesel, brake thermal efficiency provides optimal efficiency and low brake specific fuel consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diesel engines

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Detection of Depression from IoMT Time Series Data using UMAP features 3

Detection of Depression from IoMT Time Series Data using UMA...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Misgar, Muzafar Mehraj Bhatia, M.P.S. Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665462006

Depression is the most serious and prevalent public health problem that has led to an increase in suicidal risks, suicidal ideations, and much more impact on family relations. A diagnosis of mental health problems lacks a direct quantitative test. Conventionally experts evaluate the symptoms and signs through patient interviews or questionnaires. Data generated by wearable IoMT devices (e.g. wrist actigraph) provides an efficient objective way to diagnose depression and related mental health problems in the early stages. In this paper, we propose UMAP (Uniform Manifold Approximation and Projection) based feature extraction and neural network classifier for depression detection (both Unipolar and bipolar disorders) from IoMT (Internet of Medical Things) data (Depresjon Dataset). The classification of depression from 24-hour motor activity data shows a sensitivity of 96.257% and a specificity of 98.50%. © 2022 IEEE.

关键词： Health risks

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SleepQual and ***: Smartwatch and Smartphone based Behavioral Datasets of Youth 12

SleepQual and ***: Smartwatch and Smartphone based Behaviora...

引用

12th International Conference on Cloud Computing, Data Science and engineering, Confluence 2022

作者： Arora, Anshika Chakraborty, Pinaki Bhatia, M.P.S. Netaji Subhas University Of Technology Department Of Computer Science And Engineering New Delhi India

ISBN: (纸本)9781665437011

Wearable sensors recording different components of peoples' daily activity are commonly used these days for collecting motion data. These provide objective measures of behavioral components like sleep and physical activity. Smartphones are being used increasingly contributing to users' screen viewing and hence are capable of providing precise measures of users' screen time. Disruption in the key lifestyle patterns like sleep, physical activity and screen viewing may result in health impairments as these correspond to the utmost engaged times during the day. This study presents a novel dataset containing attributes of physical activity, sleep, and smartphone-based screen viewing collected from users' smartphones and smartwatches. The dataset contains real time objective measures of activities of 24 undergraduate students. A sleep quality indicator and a behavioral health indicator namely SleepQual and *** respectively are evaluated using the extracted features. The instances are labelled based on the scores of SleepQual and ***. We have made the dataset publicly available which opens avenues for researchers to employ various intelligent data analysis techniques in order to develop systems capable of automatically assessing sleep quality and behavioral health using digital data without the need of any self-reporting questionnaires and tools. To provide baseline performance four machine learning classifiers are implemented on the proposed dataset. © 2022 IEEE.

关键词： Smartphones

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A Review on security schemes for Electronic Health Records 5

A Review on security schemes for Electronic Health Records

引用

5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Negi, Lokesh Bhatt, Shobha Netaji Subhas University of Technology Department of Computer Science & Engineering New Delhi India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

An electronic health record is a repository of patient's information in the digital form. Nowadays, EHRs are heavily used instead of paper-based records because of their numerous benefits. But there are security and privacy challenges too while using electronic health records as it includes very sensitive information of patients like symptoms, lab results, diagnoses, immunizations, medications, and diagnostic reports. Various schemes have been designed to resolve the issue of security and privacy of EHRs in the healthcare organisations. This paper reviews the existing schemes which have been proposed to safeguard the security and privacy of EHRs from the malicious attacks. The paper categorizes the existing security schemes into four categories on the basis of security mechanism and present schemes of each category. © 2022 IEEE.

关键词： Diagnosis

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Non-linear Fractional Order Fuzzy PD Plus I Controller for Trajectory Optimization of 6-DOF Modified Puma-560 Robotic Arm

Non-linear Fractional Order Fuzzy PD Plus I Controller for T...

引用

Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Varshney, Himanshu Yadav, Jyoti Chhabra, Himanshu Jharkhand Dhanbad India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India Department of Mechatronics Parul Institute of Technology Parul University Gujrat Vadodara India

ISBN: (纸本)9789819955206

The purpose of this research is to employ non-integer order calculus to enhance the control action of the non-linear fractional order fuzzy PD plus I (FOFPD + I) controller. To operate a non-linear 6-DOF Puma 560 robotic arm, a FOFPD + I controller is developed and implemented. Fractional order fuzzy proportional derivative (FOFPD) and fractional order fuzzy integral (FOFI) controllers are used to create the proposed controller. Because of the non-linear gains, the proposed control approach preserves the linear structure of the fractional order proportional derivative plus integral (FOPD + I) controller while still providing adaptive capabilities. Further, the PID controller is also derived to compare with the FOFPD + I controller. Both, FOFPD + I and PID controller’s parameters are optimized using non-dominated sorting genetic algorithm-II (NSGA-II). The performance and effectiveness of the presented controller are examined in terms of trajectory tracking, tracking error, and robotic arm control efforts. Simulation shows excellent performance of FOFPD + I controller over traditional PID and fuzzy logic controllers. To be precise, on the current optimal solution, suggested controlling strategy is more than 80% efficient than other strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Controllers

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Condition Monitoring & Fault Detection in Photovoltaic Modules Using Machine Learning 1

Condition Monitoring & Fault Detection in Photovoltaic Modul...

引用

1st International Conference on Sustainable technology for Power and Energy Systems, STPES 2022

作者： Tyagi, Shivam Dhingra, Bhavya Tomar, Anuradha Netaji Subhas University of Technology Department of Computer Science Delhi78 India Netaji Subhas University of Technology Department of Electrical Engineering Delhi78 India Netaji Subhas University of Technology Department of Inst. and Control Engg. Delhi78 India

ISBN: (纸本)9781665459150

There has been a significant increase in the use of renewable energy solutions such as Photovoltaic (PV) power plant projects to decrease the dependency on fossil fuels while still meeting the global energy demands. However, these technologies come with their own set of problems. Condition monitoring and maintenance of PV modules are one of the challenges that need to be tackled to make these new energy solutions even more efficient. The cleaning of PV modules and the maintenance of hardware components used in a power plant are significantly important to the total energy yield and efficiency of the project. This paper provides a detailed look into the application of machine learning algorithms for condition monitoring and fault detection in photovoltaic modules. This paper employs various regression models to predict the power output of the PV modules accurately. The model takes inputs from the inverter connected to an array mesh of PV modules and applies a trained prediction model that can work in real-time. The system is based on comparing linear, Bayesian and non-linear regression models on their ability to explain the variance of the yield by PV modules. © 2022 IEEE.

关键词： Fault detection

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Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs) 6th

Identification of Real or Fake Currency Using Semi-supervise...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Mukherjee, Anupam Ghosh, Anupam Department of Computer Science & Engineering Siliguri Institute of Technology West Bengal Siliguri India Department of Computer Science & Engineering Netaji Subhash Engineering College West Bengal Kolkata India

ISBN: (纸本)9789819780891

Traditional paper money and contemporary electronic money are two significant forms of exchange. However, due to the new and improved methods that counterfeiters are using, it is now becoming an increasingly important issue. This work suggests a machine-assisted method that has been created to distinguish between fake and real currency. Generative adversarial networks (GANs), a cutting-edge machine learning model, are used for this purpose. To develop a model that can be used for supervised prediction, GANs utilize semi-supervised learning. Semi-supervised learning is an exceptional approach in building a model that is fit for supervised prediction. It offers a multitude of benefits, allowing the training of unlabeled data while achieving highly accurate predictions. In this work, detecting counterfeit money using GANs involves a multi-step methodology that leverages the power of deep learning for accurate identification. Preprocess the data by resizing, normalizing, and augmenting the images to ensure uniformity and improve model generalization. The discriminator minimizes the loss when making correct classifications, while the generator minimizes the loss when the discriminator classifies its generated images as genuine. Indian banknotes were subjected to this procedure. Modern feature identification and image processing techniques were employed to develop the overall strategy of a valid input. The trials demonstrate that a high-precision system may be created to identify real paper money. During the training process, the generator network and the discriminator networks update their loss functions. Once training is complete, the model can accurately identify real money notes from fake ones with an 80% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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