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2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Arulprakash, A. Ponmaniraj, S. Gokul Rajan, V. Divya, A. Sathyamaba Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam602 105 India School of Computing Science and Engineering Galgotias University Greater Noida India Vandayar Engineering College Department of Computer Science and Engineering Pulavar Naththam Thanjavur India

ISBN: (纸本)9798350394580

Aeras of investigational and clinical study have demonstrated several pathways in the progression of Alzheimer's disease (AD), but no treatments have been developed. Patient lifestyle, clinical, and biological data are increasingly being collected through open data-sharing programmes, and the resulting data set may be infinite in size, far exceeding the capacity of humans to make sense of it. Recent advances in artificial intelligence have opened the door to the possibility of using analysis of medical imagery to aid in the analysis of Alzheimer's disease. A variety of methods, including those based on artificial intelligence, have been devised and used in several research to aid in the CAD of AD identification. The use of AI methods to solve Alzheimer's disease diagnosis challenges in brain imaging will be a major focus of future research in this field. This article delivers an impression of the many images processing, feature extraction, and machine learning approaches that are used to solve the AD detection problem. The final section of the study will include a synopsis of the existing body of knowledge as well as a critical appraisal of potential future research avenues and open questions. © 2023 IEEE.

关键词： Alzheimer's disease detection Artificial intelligence diagnosis machine learning U-Net

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Survey on Finger Vein Recognition using Convolutional Neural Network 2

Survey on Finger Vein Recognition using Convolutional Neural...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Pandey, Surya Prabhu, Nagendra Kumar, Adithya Suresh New Horizon College of Engineering Computer Science and Engineering Bangalore India Srm Institute of Science Technology Kattankulathur School of Computing Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9781665400527

Vein identification, also known as vascular technology, it is a biometric identification approach that analyses the blood patterns visible on the skin's surface. Despite the fact that it is used by FBI and CIA for community security and criminal investigations, such as forensic investigation and law enforcement, this recognition method is still in development. Vein recognition technology offers great solutions to challenges. Some challenges are ID or password leakage and then reusing it, lost smart cards or keys. Finger Vein Recognition can become most promising for high security applications. Our paper explains about the different cutting-edge machine learning algorithms that are applied so far. At the end, a comparative analysis is provided. © 2022 IEEE.

关键词： Machine learning

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Using a research-based assessment instrument to explore undergraduate students’ proficiencies around measurement uncertainty in physics lab contexts

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Physical Review Physics Education Research 2024年第2期20卷 020105-020105页

作者： Gayle Geschwind Michael Vignal Marcos D. Caballero H. J. Lewandowski Department of Physics Department of Physics and Astronomy and CREATE for STEM Institute Department of Computational Mathematics Science and Engineering Department of Physics and Center for Computing in Science Education

Concepts and practices surrounding measurement uncertainty are vital knowledge for physicists and are often emphasized in undergraduate physics laboratory courses. We have previously developed a research-based assessment instrument—the Survey of Physics Reasoning on Uncertainty Concepts in Experiments (SPRUCE)—to examine student proficiency with measurement uncertainty along a variety of axes, including sources of uncertainty, handling of uncertainty, and distributions and repeated measurements. We present here initial results from the assessment representing over 1500 students from 20 institutions. We analyze students’ performance pre- and postinstruction in lab courses and examine how instruction impacts students with different majors and gender. We find that students typically excel in certain areas, such as reporting the mean of a distribution as their result, while they struggle in other areas, such as comparing measurements with uncertainty and correctly propagating errors using formulas. Additionally, we find that the importance that an instructor places in certain areas of measurement uncertainty is uncorrelated with student performance in those areas.

关键词： Assessment Instructional strategies Student preparation

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Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model 1

Measuring COVID-19 Opinion in the Online Debate using an Uns...

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1st IEEE International Conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Priyadarsini, K. Jeba Sonia, J. Vijayalakshmi, K. Anbazhagu, U.V. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9781665460842

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

关键词： COVID-19

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Mandatory Automated Safety Kernel (MASK): Face Mask Recognition Using Deep Learning Computer Vision 2

Mandatory Automated Safety Kernel (MASK): Face Mask Recognit...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Dinesh, G. Singh, Amarjeet Shinz, Nehar Sravanya, Mogalluru Venkata Agrawal, Gaurangi Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781665400527

This paper involves utilizing modules to create an efficient and accurate machine learning face mask detection model that can give an accuracy close to 100 percent and a value loss of less than 5%. This paper sees up to the fact that it is the local clusters that need to take care of themselves and this is what will eventually help the society on the larger scale as a whole and aims to aid the concerning authorities in keeping their crowd wearing masks at their respective premises. This is done by programming a system that can recognize the visuals from a camera at the entrance of such premises flagging offenders who do not wear a face mask and notifying authorities as it happens. This can be implemented in private organizations like educational institutions or company offices making it possible to combine it with a facial recognition model thereby notifying the offenders themselves via their cell phones by fetching their information from database resources that would be already existing in the case of these private organizations. © 2022 IEEE.

关键词： Face recognition

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Computer Aided Diagnosis Multi-model System using Late Fuison and Ensemble Learning

Computer Aided Diagnosis Multi-model System using Late Fuiso...

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Pervasive computational Technologies (ICPCT), International Conference on

作者： M.Kani Priya Gunabhiram Aruru Nikhil Vanam M. Suganiya M. Salomi Department Of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Lung Cancer and brain tumors are the well-known causes of cancer deaths universal. Therefore, appropriate and accurate diagnosis is an important issue that affects better and more reliable treatment and the patient's chance of survival. Present diagnostic strategies are mostly based on a single piece of information. This often leads to missing necessary information, which can lead to Misidentification of condition or delay in timely treatment. This study presents a novel Computer-Aided-Diagnosis system designed to enhance the detection of lung cancer and brain tumors by integrating multimodal data, including medical images (CT and MRI scans) and clinical information. The proposed approach employs late fusion and ensemble learning techniques, utilizing CNN for processing CT images, VGG19 for analyzing MRI scans, and Random Forest classifiers for evaluating clinical data. By separately training these models, the system capitalizes on the distinct characteristics of each data modality, overcoming the limitations of traditional CAD systems that typically rely on a single data source. This comprehensive analysis not only improves diagnostic accuracy but also provides reliable predictions, ultimately facilitating better clinical decision-making for lung cancer and brain tumor detection.

关键词： Solid modeling Computed tomography Magnetic resonance imaging Decision making Lung cancer Brain tumors Brain modeling Data models Ensemble learning Medical diagnostic imaging

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