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An Improved Deep Structure for Accurately Brain Tumor Recognition

computer Systems Science & engineering 2023年第8期46卷 1597-1616页

作者： Mohamed Maher Ata Reem N.Yousef Faten Khalid Karim Doaa Sami Khafaga Department of Communications and Electronics Engineering MISR Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Electronics and Communications Delta Higher Institute for Engineering and TechnologyMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Brain neoplasms are recognized with a biopsy,which is not commonly done before decisive brain *** using Convolutional Neural Networks(CNNs)and textural features,the process of diagnosing brain tumors by radiologists would be a noninvasive *** paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning *** proposed model extracts Gray Level Co-occurrence Matrix(GLCM)textural features from MRI brain tumor ***,a deep neural network(DNN)model has been proposed to select the most salient features from the ***,it manipulates the extraction of the additional high levels of salient features from a proposed CNN ***,a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition *** common datasets have been applied and tested,Br35H and FigShare *** first dataset contains binary labels,while the second one splits the brain tumor into four classes;glioma,meningioma,pituitary,and no ***,several performance metrics have been evaluated from both datasets,including,accuracy,sensitivity,specificity,F-score,and training *** results show that the proposed methodology has achieved superior performance compared with the current state of art *** proposed system has achieved about 98.22%accuracy value in the case of the Br35H dataset however,an accuracy of 98.01%has been achieved in the case of the FigShare dataset.

关键词： Brain tumor convolutional neural network gray level co-occurrence matrix noninvasive FigShare dataset Br35H dataset

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Efficient Method for Detecting Abnormal Growth of Blood Vessels Using Convolutional Neural Network

Efficient Method for Detecting Abnormal Growth of Blood Vess...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Kumar, A. Deepak Sasipraba, T. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600 119 India Department of Computer Science and Engineering St. Joseph s Institute of Technology Chennai600119 India Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350334357

Diabetic Retinopathy is the main problem in human life because of high sugar levels present in the blood. Various organs get affected due to this reason. The eye is the one of the parts of the human eye which goes to vision problems sometimes blindness. The early stages of detection need to protect the human eye. Existing model uses various methods which do not solve the problem completely. Machine learning based approach introduced for detection of the affected area of eye. The Blood Vessels of the eye get affected as a result bleeding in the eye and excess growth in the eye. Traditional algorithms are not suitable for detecting this growth rate due to the less resolution images. The CNN based model with integrated data sets are used to classify and detect the blood vessel. The high resolution images are used for detecting the location and exact difference in normal vessels. Various algorithms are used with different data sets for making multidimensional analysis. The objective of this method is to identify the heterogeneous vessels from the normal vessel with a high level of accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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Pulmonary Nodule Detection in Computed Tomography Images Using Deep Learning 1

Pulmonary Nodule Detection in Computed Tomography Images Usi...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Longkumer, Akumlong Walling, Supongmen Kalita, Shobhanjana Icfai University Department of Computer Science Dimapur India National Institute of Technology Department of Computer Science and Engineering Dimapur India Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350348057

the well-being of individuals is seriously threatened by lung cancer, which has one of the highest fatality rates of all cancers. Therefore, to increase patient life expectancies, early diagnosis of lung nodules is essential. For many years, the science of machine vision and medical diagnostics has used convolution neural networks. Early diagnosis can significantly lower the patient's mortality, and computer-aided diagnosis has emerged as a vital tool for medical professionals. computer-aided diagnosis depends on the ability to identify pulmonary nodules in computed tomography images. In the Computed Tomography scans, it is difficult to find the lump inside the lungs. This project uses U-Net to implement automated lung nodule identification. Furthermore, the data sets used for pulmonary nodule recognition from CT scan images are from the freely accessible LIDC/IDRI database. © 2023 IEEE.

关键词： computer aided diagnosis

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Analyse The Energy Consumption by Integrating the IOT and Pattern Recognition Technique 2

Analyse The Energy Consumption by Integrating the IOT and Pa...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rao Polamuri, Subba Nalla, Lakshmi Madhuri, A Durga Kalagara, Surendranath Subrahmanyam, Bonthu Aparna, Peyyeti.B.L. Odalarevu India Department of Computer Science and Engineering Odalarevu India Department of Science and Humanities Odalarevu India Bonam Venkata Chalamayya College of Engineering Department of Computer Science and Engineering Palacharla Rajahmundry India Bonam Venkata Chalamayya Institute of Technology & Science Department of Computer Science and Engineering Bhatlapalem India

ISBN: (纸本)9798350371055

High power costs and end-use electricity usage are driving energy consumption optimization and data-driven demand-side management. The consumer action and the approach used to energy management have a significant impact on how well these systems work. It is crucial to provide comprehensive energy-consumption information models down to the appliance level to educate the public and encourage them to adopt more efficient energy habits. The Home Energy Management System's (HEMS) primary objective is to cultivate an energy-efficient environment that controls IoT devices via its network. With HEMS, customers may adjust their use to day-to-day price fluctuations, thus reducing energy expenses. expressive data-mining methods to create a numerical model that provides users with data about their home appliances. This data includes the number and length of operations, energy consumption over different periods based on 15-minute monitoring data, and the ability to disaggregate cycles for appliances with cyclic operation. Calibration and validation of the model were performed on two datasets acquired by ENEA via actual monitoring of the Italian home. Subsequent testing across many appliances demonstrated successful analysis of energyingesting decorations. Consequently, it has been included in the ENEA-developed DHOMUS IoT stage, which tracks and analyses residential energy use to raise public participation and knowledge about this issue. Encourage end users to make more ethical and sustainable energy use decisions and meet the current European Council Regulation (EU) 2021/1853 requirements by reducing energy consumption, according to the findings it shows that the model built is sufficiently accurate. © 2024 IEEE.

关键词： EU HEMS Home Appliance IOT Platform Monitoring Data

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Developing an ML Model for Detecting the Cyber Attacks in Electric Vehicles 1

Developing an ML Model for Detecting the Cyber Attacks in El...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Roopesh Albert, Bertia Manuel, Alan Shine Hemram, Saimon Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350372748

This study shows that electric cars (EVs) are a viable replacement for conventional combustion-engine vehicles, providing lower carbon emissions and more environmentally friendly modes of transportation. Depite the hype, the challenges like cybersecurity flaws and possible cyberattacks are also emerging in EVs. In addition to jeopardizing user privacy and vehicle safety, a number of issues seriously jeopardize the integrity and security of electric cars (EVs). This research offers a unique machine learning (ML)-based detection and prevention system designed to identify cyber threats in electric vehicles (EVs) in response to these problems. The suggested solution examines Controller Area Network (CAN) traffic data using cutting-edge ML algorithms to find unusual patterns suggestive of cyber threats. The solution improves the cybersecurity resilience of electric vehicles (EVs) by including proactive preventive measures and real-time threat detection capabilities. This ensures vehicle safety and guards against new and existing cyber-attacks. Through thorough evaluation and validation procedures, the efficacy of the suggested system is proven, underscoring its strong performance in identifying and averting cyberattacks in electric vehicles. This research helps to create safer and more secure mobility solutions for electric vehicles by implementing proactive cybersecurity measures and integrating cutting-edge ML technology. © 2024 IEEE.

关键词： Crime

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Analysis of Cs2TiBr6 Single-Halide Perovskite Solar Cell by Introducing IDL 24

Analysis of Cs2TiBr6 Single-Halide Perovskite Solar Cell by ...

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24th IEEE International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2024

作者： Kaur, Jaspinder Gupta, Syamantak Basu, Rikmantra Sharma, Ajay Kumar National Institute of Technology Delhi Department of Electronics and Communication Engineering New Delhi India National Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350390537

Lead-based perovskite solar cells (PSCs) are popular in the photovoltaic industry for their remarkable properties, but issues like toxicity and instability limit their use. To address these problems, eco-friendly, lead-free, and stable Cesium Titanium Bromide (Cs2TiBr6) absorber material has been introduced. This work simulates an FTO/SnO2/IDL1/Cs2TiBr6/IDL2/MoOX/Au structure using SCAPS-1D incorporating the use of interfacial defect layer (IDL), examining factors such as layer thickness, doping, defect density, and quantum efficiency. The optimized structure achieves a power conversion efficiency (PCE) of 20.11%, having a fill factor of 82.17, VOC of 1.488 V, and JSC of 16.34 mA/cm2. This research supports the development of efficient, low-toxic, and stable PSCs for the solar industry. © 2024 IEEE.

关键词： Perovskite solar cells

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Decoding SARS-CoV-2 Variants: An in-silico approach to RNA-Seq feature extraction using K-mers and N-grams

Decoding SARS-CoV-2 Variants: An in-silico approach to RNA-S...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Iqbal, Naiyar Bhardwaj, Aditya Maulana Azad National Urdu University Department of Computer Science and Information Technology Telangana Hyderabad India International Institute of Information Technology Department of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350348460

The world's enormous challenge is to combat the COVID-19 pandemic, which has spread by the SARS-CoV-2 virus over the past three years. Many waves are formed with diverse variations categorized as variants of concern and variants of interest by the World Health Organization (WHO). Moreover, the emergence of a new coronavirus and its susceptibility to various pandemic conditions throughout the world strongly imply that the virus is taking new shapes, resulting in divergent variations, and predicting the virus becomes more ***: This article proposes an integrated intelligent prediction model to predict coronavirus variants of concern, such as alpha, beta, gamma, delta, and omicron, using RNA-Seq and machine learning ***: We proposed machine learning-based SARS-CoV-2 variants prediction model with 2028 RNA-Seq of alpha, beta, gamma, delta, and omicron were gathered to train prominent classifiers for the prediction of any unseen sequences of these variants. In this order, the feature vector was created from the motif of the sequence obtained through K-mer and N-gram methodologies to build prediction ***: The findings show that the developed prediction model based on Logistic Regression (LR) performs better by reaching an aggregated accuracy of 99% during training and testing compared to other models. We also validated the models using unseen variant sequences and found a significant prediction ***: Our experiment showed that the suggested SARS-CoV-2 variants prediction model is capable of predicting the SARS-CoV-2 variants of concern using a logistic regression trained model. © 2024 IEEE.

关键词： Classification Omicron RNA-Seq SARS-CoV-2 Text Mining Variants of Concern

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Digital Secure Storage for Healthcare Data Information Using Blockchain 3

Digital Secure Storage for Healthcare Data Information Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naveenkumar, H. Ranjana, P. Hindustan Institute of Technology & Science Department of Computer Application Chennai603103 India Hindustan Institute of Technology & Science Department of Computer Science Engineering Chennai603103 India

ISBN: (纸本)9798350399264

Healthcare Blockchains grant access to a modern technique to accumulate healthcare information, achieve healthcare, and make a belief for medical records distribution and records combination in a decentralized unlock healthcare network surroundings. Even though the healthcare blockchain technologies have paid attention to extroverted benefits in addition to a concentration in assiduousness, management as well as university circles, the safety and confidentiality concerns stay behind the center of attention of deliberate while organizing Blockchains for information sharing across company operations and research collaboration in the healthcare sector. Medical data sharing via Blockchains are the subject of research that focuses on security and privacy needs. Together with technological solution techniques and strategies, the research provides a complete evaluation of the safety and confidentiality risks, requirements, and technology solutions. In the beginning, we discuss the security and privacy requirements for using healthcare Blockchains to share electronic medical records. Second, we group current initiatives into three reference blockchain procedure scenarios for the sharing of electronic medical records. We consider the technologies that can be used to implement this secure store for healthcare data properties in the two categories of healthcare Blockchains procedure scenarios, including anonymous signatures, and attribute-based encryption. Source to implement the access control for hospital, patient, doctor, admin. © 2023 IEEE.

关键词： Blockchain

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