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A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network

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Measurement: sensors 2023年 27卷

作者： Ravishankar, t. Nadana Makarand Jadhav, Hemlata satheesh Kumar, N. Ambala, srinivas Pillai N, Muthuvairavan Department of Data Science and Business Systems School of Computing SRM Institute of Science & Technology KTR Tamil Nadu Chennai India Department of E & T C Marathwada Mitra Mandal's College of Engineering Pune India Department of Electronics and Communication Engineering PBR Visvodaya Institute of Technology and Science Kavali Andhra Pradesh 524201 India Computer Engineering Department Pimpri Chinchwad College of Engineering Sector -26 Pradhikaran Nigdi Maharashtra Pune411 044 India Department of Computer Science and Business Systems R.M.D Engineering College Kavarapettai India

Most women generally have an ovarian cyst, causing the disorder. Pregnant cysts occur when many water-packed tumors appear in the womb. this is especially true for women who have a worthy cause for childbearing. Women are related to menstrual problems and cyst problems during pregnancy. Recently, ultrasound imaging and machine learning techniques have been used to detect ovarian cysts. Different domain experts provide their own decisions on finding out what kind of ovarian cyst it is from ultrasound images. However, a most accurate and uniform decision-making system is necessary for the early detection of cysts. to aid physicians, an automated detection system has been proposed in this paper to make it more effective for physicians to eliminate these problems. this system uses the extracted features from the image for cysts detection and classification. Automatic ovary cyst detection (OCD) and classification are implemented in this work using a fuzzy rule-based Convolutional Neural Network (FCNN). the proposed system (OCD-FCNN) has yielded 98.37% accurate results when tested with benchmark datasets. © 2023 the Authors

关键词： Fuzzy inference

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Electric-Field Programmable spin Arrays for scalable Quantum Repeaters

Electric-Field Programmable Spin Arrays for Scalable Quantum...

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CLEO: QELs_Fundamental science, QELs 2022

作者： Wang, Hanfeng trusheim, Matthew E. Kim, Laura Englund, Dirk Research Laboratory of Electronics M.I.T. CambridgeMA02139 United States Department of Electrical Engineering and Computer Science M.I.T. CambridgeMA02139 United States U.S. Army Research Laboratory Sensors and Electron Devices Directorate AdelphiMD20783 United States

We introduce an electric-field programmable spin array (FPsA) for scalable quantum repeaters. We analyze the FPsA scheme for arrays of nitrogen vacancy color centers in a slow-light diamond waveguide. © Optica Pu... 详细信息

ISBN: (纸本)9781557528209

关键词： Electric fields

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Enhancing Fruit and Vegetable Preservation with support Vector Machine and iot Connectivity

Enhancing Fruit and Vegetable Preservation with Support Vect...

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Automation and Computation (AUtOCOM), international Conference on

作者： D. satyaraj t. Prabakaran Y. M. Blessy L. N. Jayanthi s. K. Mouleeswaran s. Velmurugan Department of Electronics and Communication Engineering Arjun College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Computer Science and Engineering Dayananda Sagar University Innovation Campus Bangalore Karnataka India Department of Electronics and Communication Engineering T.J.S. Engineering College Channai Tamil Nadu India

this paper presents a new method for extending the shelf life of perishable food items by combining the use of support Vector Machine (sVM) algorithms with internet of things (iot) connection. Problems with early spoiling detection and inefficient storage condition control plague conventional approaches to fruit and vegetable preservation. Using sVM it analyze environmental variables including humidity, temperature, and gas concentrations that impact commodities with a near-term expiration date in real time via this research. sensors, actuators, and a command centre can all communicate to one other without a hitch thanks to the iot. With this integration, storage conditions may be dynamically adjusted according to sVM predictions, guaranteeing the best possible preservation. in light of the ever-changing nature of farming, the suggested system provides an adaptable and scalable answer to the growing need for effective and environmentally friendly food storage solutions. the experimental findings show that the suggested method may prolong the freshness and quality of produce while reducing the amount of resources wasted. the findings of this study will help bring data-driven technology into the food business more widely and improve smart agriculture practices. it highlights the efficacy of combining iot connection with sVM algorithms for vegetables and fruits storage. Agricultural operations and customers may both profit from the results, which show a considerable decrease in waste, an increase in freshness, and better quality control.

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Optimized image Enhancement Enabled LstM technique for Plant Leaf Disease Detection 9

Optimized Image Enhancement Enabled LSTM Technique for Plant...

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9th international Conference on science, technology, engineering and Mathematics, iCONstEM 2024

作者： sanchez, Domenic t. Raghuvanshi, Abhishek Biban, Laxmi Prabhu, P. Godara, sunila Garg, Rachit Cebu Technological University-NEC Cebu Philippines Mahakal Institute of Technology Department of Cse Ujjain India Pt. C.L.S. Govt. College Department of Botany Haryana Karnal India Alagappa University Information Technology Centre For Distance And Online Education Karaikudi India Gjus&t Cse Department Hisar India School of Computer Science And Engineering Lovely Professional University India

ISBN: (纸本)9798350365092

Diagnosing plant diseases is one of the most crucial and essential tasks in agriculture. Visual inspection or microscope is frequently used to identify causes. Visual judgment is subjective, and mistakes, biases, and optical illusions can arise due to the effect of cognitive and psychological processes and the subjective character of the job. Molecular biology, immunology, and pathogen culture-based laboratory studies sometimes take too long and do not produce results quickly enough. in light of this, automated technology's ability to quickly and accurately diagnose illness is essential. For big farms, manual observation is impracticable and costly since it requires constant plant monitoring. this article presents an optimized image enhancement enabled LstM technique for plant leaf disease detection. Leaf images data set is used as input. the noise in these leaf images is Gotten rid of by employing an adaptive filter during the preprocessing stage of the development process. the Particle swarm methodology is used by the LstM optimization method in order to get a finer level of control over the input parameters. Additionally, Grey Wolf Optimization is used in order to enhance the functionality of the input parameters of the LstM classifier. the approach known as the Gradient Boosting Decision tree is further used for classification purposes. For the purpose of the experiment, the PlantVillage data set is used. total 2500 images are selected at random for the experimental work. two hundred photographs are used in order to train the classification model, and the remaining five hundred images are used in order to test the classification model after it has been trained. in the process of classifying leaf images, the PsO LstM algorithm is becoming more accurate. PsO LstM has achieved accuracy of 99 percent. © 2024 iEEE.

关键词： Adaptive filters

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An Advanced Cognitive Approach for Personalized Drug Formulation in type 2 Diabetes Using Deep Neural Networks and Feature-based Fusion

An Advanced Cognitive Approach for Personalized Drug Formula...

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intelligent Control, Computing and Communications (iC3), international Conference on

作者： Brijendra Pratap singh Jagendra singh Madhuri suryavanshi Jailani s A. beer A. Amer Rakesh Kumar School of Computer Science Engineering & Technology Bennett University Greater Noida India Pimpri Chinchwad College of Engineering Pune India R&D Formulation Dev. Alpha Pharma KAEC Rabigh Saudi Arabia Sadat academy for management and Sciences Alexandria Egypt Department of Computer Science and I.T. Magadh University Bodh Gaya India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Personalized medicine is an innovative approach toward achieving improved therapeutic outcomes, notably in chronic diseases like type 2 Diabetes (t2D), where individualised drug formulations represent the crux of treatment. Conventional methods of drug prescription tend to focus on generic approaches without considering the specific genetic, physiological, and lifestyle characteristics of individuals. this research fills this gap by developing an advanced deep learning-based method for personalised drug formulation in t2D. it integrates diverse patient data, including demographics, medical history, clinical measurements, genomic information, pharmacokinetic information, real-time monitoring data, and responses to previous treatments. For optimal drug formulation, it makes use of a combination of deep learning models, such as Recurrent Neural Network (RNN) for time-series prediction, Convolutional Neural Network (CNN) for genomic analysis, GAN for producing synthetic data, and auto-encoder for feature extraction. the dataset consists of 2700 patient records collected over one year, wherein the pre-processing phase includes handling missing values, normalising continuous variables, and encoding categorical features. trained models were assessed based on precision, recall, F1-score, and AUC ROC levels; the best performance came from the RNN model with a maximum accuracy of 97.35%. CNN, GAN, and Autoencoder also obtained similar competitive results. However, the RNN-based model received the highest score in recommending the appropriate drugs. the application of this research can contribute to the broader discipline of precision medicine.

关键词： Drugs Recurrent neural networks Accuracy Autoencoders Genomics Generative adversarial networks Real-time systems Diabetes Convolutional neural networks Bioinformatics

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ML_sPs: stroke Prediction system Employing Machine Learning Approach 1st

ML_SPS: Stroke Prediction System Employing Machine Learning ...

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1st international Conference on Artificial intelligence and Data science, iCAiDs 2021

作者： Hossain, Md. sazzad shovo, Mehedi Hassan Ali, Md. Mamun Nayer, Md. Ahmed, Kawsar Bui, Francis M. Department of Computer Science and Engineering Daffodil International University SukrabadDhaka-1207 Bangladesh Department of Software Engineering Daffodil International University SukrabadDhaka-1207 Bangladesh Department of Computer Science and Engineering B.I.T. Patna Campus Samanpura BIT Patna Bihar800014 India Group of Bio-Photomatix Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail1902 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus 9 Drive SaskatoonSKS7N 5A9 Canada

ISBN: (纸本)9783031213847

stroke is a dangerous health issue that happens when bleeding valves in the brain get damaged. and blood supply to the brain is cut off. As a result of these factors, numerous body parts may cease to function. stroke is currently a significant risk factor for mortality, as per World Health Organization (WHO). it may be preferable to mitigate the severity of stroke by detecting it early. in recent years, data science has been critical to the growth of research in the medical field. Various machine learning techniques are built employing a patient’s physical and physiological reporting data to forecast the risk of stroke. in this article, we use five machine learning approaches to find the best effective model that can predict the risk of stroke, including Decision tree (Dt), XGBoost, Light Gradient Boosting Machine (LGBM), Random Forest (RF), and K-nearest Neighbors learning. Kaggle was used to collect the dataset. Random forest produces acceptable findings with an accuracy of roughly 96%, which might be included in actual clinical information. the machine learning approach can aid in the prediction of many diseases like a stroke in the early stages. © 2022, the Author(s), under exclusive license to springer Nature switzerland AG.

关键词： Forecasting

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improving Data Encoding for Enhanced Ai Performance in Complex Datasets via Quantum Feature space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

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Pervasive Computational technologies (iCPCt), international Conference on

作者： Gurpreet singh Walia sathiya Priya s KV Karthikeya D. suresh P sudheer V. syambabu Lead Enterprise Security and Networking Architect NetApp Inc. Durham NC Department of ECE Panimalar Engineering College Chennai AT&T Sattva Knowledge City Hyderabad Department of ECE St. Joseph's Institute of Technology Chennai Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

the purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (Ai) systems that are capable of managing complicated datasets. traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. this can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. this makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. the purpose of this research is to investigate the application of quantum algorithms, such as Quantum support Vector Machines (QsVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： support vector machines Quantum computing Quantum algorithm Quantum entanglement Computational modeling space mapping Quantum state Encoding Hardware Quantum circuit

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Hacking wireless network credentials by performing phishing attack using Python scripting 5

Hacking wireless network credentials by performing phishing ...

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5th international Conference on intelligent Computing and Control systems, iCiCCs 2021

作者： Musthyala, Harish Reddy, P. Nagarjuna University of New Haven Department of Computer Science CT United States K.I.T.S. Department of Electrical and Electronics Engineering Warangal India

ISBN: (纸本)9781665412728

the availability of number of open-source hacking tools over the internet and many hacking tools in-built with the Kali Linux operating system led to easy understanding and performing hacking by individuals. Even though, hacking the Wi-Fi passwords is considered a tedious task with open-source tools, they can be hacked easily with phishing. Phishing involves tricking the users with malicious emails and obtaining sensitive information from them. this paper describes the different wireless security protocols and tools for hacking wireless networks. A python script is developed which can be sent as phishing to get all the ssiD's and passwords to which the system has been connected. the script has been executed and the results are presented. © 2021 iEEE.

关键词： Python

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EEG and HRV-Based Assessment of Neurosurgeons training for Anxiety Regulation and stress Monitoring 2

EEG and HRV-Based Assessment of Neurosurgeons Training for A...

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2nd Edition iEEE international Conference on Metrology for eXtended Reality, Artificial intelligence and Neural engineering, MetroXRAiNE 2023

作者： Arpaia, Pasquale Carone, Giovanni Castelli, Nicolò D'Errico, Giovanni Gargiulo, Ludovica Maffei, Luigi Mastrati, Giovanna Moccaldi, Nicola Nalin, Marco Perin, Alessandro Picciafuoco, Mauro Puttilli, Cosimo Ramos, Pedro M. Robbio, Rachele University of Naples Federico Ii Department of Electrical Engineering and Information Technology Naples Italy University of Naples Federico Ii Naples Italy I.R.C.C.S. Carlo Besta Neurological Institute Milan Italy Polytechnic University of Turin Department of Applied Science and Technology Turin Italy Instituto Superior Técnico University of Lisbon Department of Electrical and Computer Engineering Lisbon Portugal University of Salento Department of Engineering for Innovation Lecce Italy Ab Medica Spa Milan Italy

ISBN: (纸本)9798350300802

A neurofeedback (NF) -supported training is proposed to enable neurosurgeons to learn how to regulate their emotions. Electroencephalographic (EEG) signal and heart rate (HR) of 5 neurosurgeons were acquired in 4 sessions while performing NF-based emotion regulation (ER). subjects tried to counteract anxious and stressful states through NF, that is by decreasing the power in the beta band measured in the midline areas of the scalp. the assessment of the neurofeedback effectiveness was based on the use of Heart Rate Variability (HRV) in combination with EEG. therefore, EEG signal was exploited in real-time to guide the feedback and also in post processing, together with the HRV, to assess the effectiveness of the whole training. As the EEG signal is concerned, the results showed that the power in the beta band decreased within each session when the ER ability was strengthened, as reported in the literature. On the contrary, the HRV did not exhibit the expected increasing trend within each trial. When considering each the session on the whole, thus computing the HRV on a greater time interval, the HRV during the NF training is typically higher than the HRV computed during the rest and negative baseline phases. © 2023 iEEE.

关键词： EEG emotion regulation HRV Neurofeedkack surgery

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Glioma/Glioblastoma Detection in Brain MRi using Pre-trained Deep-Learning scheme 3

Glioma/Glioblastoma Detection in Brain MRI using Pre-trained...

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3rd international Conference on intelligent Computing, instrumentation and Control technologies, iCiCiCt 2022

作者： Rajinikanth, Venkatesan Kadry, seifedine Damasevicius, Robertas sujitha, R. Angel Balaji, Gangadharam Mohammed, Mazin Abed Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Vytautas Magnus University Department of Applied Informatics Kaunas44404 Lithuania St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Omr Chennai600119 India M/S Tata Consultancy Services Limited Tcsl Siruseri Sez Unit Sipcot I.T. Park TN Chennai603103 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Convolutional Neural Network (CNN) supported medicinal image examination is extensively accepted due to its reputation and improved accuracy. the investigational outcome obtained with DLs along with a chosen classifier helps to achieve better detection results than the traditional and machine-learning methods. the proposed research examines the performance of pre-trained VGG16 and VGG19 schemes in detecting the brain tumour (Glioma/Glioblastoma) grade using different pooling methods. the classification is performed using softMax with five-fold cross-validation, and the products are compared and presented. the brain tumour images considered in this study are collected from the Cancer imaging Archive (tCiA) dataset. this work considered 2000 images (1000 Glioma and 1000 Glioblastoma) of axial-plane with dimension of 224×224×3 pixels for the assessment, and the attained results are compared. the experimental outcome achieved with Python® confirms that the VGG16 with average-pooling provides a better classification accuracy (>99%) with Decision tree (Dt) compared with other methods considered. © 2022 iEEE.

关键词： Python

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