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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Dutta, Anurag Harshith, John Soni, Yash Gupta, Avdhesh Gupta, Vishan Kumar Gupta, Ankit Government College of Engineering & Textile Technology Department of Computer Science and Engineering Calcutta Serampore India Vellore Institute of Technology University Department of Computer Science and Engineering Vellore Campus Tamil Nadu Vellore India Shri Govindram Seksaria Institute of Technology & Science Department of Information Technology Indore India Ajay Kumar Garg Engineering College Department of Information Technology Uttar Pradesh Ghaziabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665475174

In the pursuit of knowledge, data is a distinct set of values that convey information, describing quantity, quality, facts, statistics, or simple units of meaning, better known as datum. Now, handling data, in the latest world is really a tedious job that includes all the principles related to data management as a valuable resource. Amidst all data management techniques, Sorting is the most important one. Sorting refers to arranging data in ascending or descending order according to linear relationships between data items. It involves mathematical rigour that causes data units to be arranged in a monotonic order by it's magnitude. In modern world of informatics, Quantum Computing have caught great heights. So, have the algorithms fused to it. Quantum computing is a type of computation whose operation can exploit quantum mechanical phenomena, such as superposition, interference, and entanglement. In this work, we would try to define a lower bound for the Computational Complexity of Sorting Algorithm, being heterogeneity by the lower bound for Quantum Search of an ordered list. Further, we would also develop a upper bound on the Sorting integrated using Grover's Search- a real time unstructured Quantum Search. © 2023 IEEE.

关键词： Sorting

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Online Banking Security: An Experimental Study of Transaction Authorization Usability

Online Banking Security: An Experimental Study of Transactio...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Prasad, J Siva Ram Kumar, K. Parish Venkata Reddy, S Srihari Chaitanya, Pagadala Ravi Kumar, E.S.R. Reddy, G Venkata Ramana V. R. Siddhartha Engineering College Department of Maths Vijayawada India Brindavan Institute of Technology & Science Department of Computer Science and Engineering Kurnool India St.Martin's Engineering College Dhulapally Department of Computer Science and Engineering Secunderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317060

The security measures used for online banking have undergone significant transformations throughout the comparatively short duration of its existence. The first implementations of authenticating and identity management systems were, and in some cases continue to be, susceptible to various assaults, including phishing. One of the current state-of-the-art options is using out-of-band channels for user re-authentication before each transaction. This article details the findings of a security analysis of such a system. According to the study results, it is secure against specific threats while being open to others. As attackers continue improving their methods, the remaining vulnerabilities will likely be taken advantage of in the future. Measures that might be taken toward against such assaults in the future are detailed. © 2023 IEEE.

关键词： Authentication

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EEG Channel Selection for Parkinson's Disease Detection Analysis using Stockwell-Transform

EEG Channel Selection for Parkinson's Disease Detection Anal...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Afonso, Melina Maria Edla, Damodar Reddy National Institute of Technology Goa Department of Computer Science and Engineering Goa Ponda403401 India Goa College of Engineering Department of Computer Engineering Goa Ponda403401 India

ISBN: (纸本)9798350313796

When the substantia nigra of the brain suffers a gradual loss of dopaminergic neurons;this is a prime diagnosis of Parkinson's disease (PD). Electroencephalography (EEG) of PD patients are an important tool for this diagnosis, as the disease pertains to the brain. Various temporal, frequency, and time-frequency domain approaches have been utilized to analyze EEG signals of patients diagnosed with Parkinson's disease. In our research, Stockwell transform (S-transform or ST), which is a time-frequency technique, is used for the analysis of channel selection for detection of PD patients from healthy controls (HCs). A transformation of the EEG signal to the time-frequency domain is carried out using ST, and this transformed signal is utilized for all operations. The S-Transform-Amplitude (STA) matrix has been calculated to obtain the frequency-maximum-amplitude (FMA) plot for five channels of the mid-brain namely, C3, C4, Pz, Fz, and Cz. This plot is evaluated to extract statistical features. The features chosen for evaluation are mean, standard deviation, kurtosis, skewness, and log-variance. Using the T-test, the p-value is calculated to find out the similarity and, hence, select appropriate channels and features. The estimated values for standard deviation and log-variance varied significantly in channels C3, Fz, and Cz for the two categories of subjects. The publicly available San Diego data set of resting state EEGs is examined. The dataset includes EEG recordings from 16 HC and 15 PD patients, who were both on and off medication at the time of recording. © 2023 IEEE.

关键词： Electroencephalography

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SSIAO: Strategic Semantic Incremental Approach for Domain Centric Open Linked Data Generation 15th

SSIAO: Strategic Semantic Incremental Approach for Domain Ce...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Deepak, Gerard Hariharan, Nitin Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Bengaluru India Alagappa Chettiar Government College of Engineering and Technology Karaikudi India

ISBN: (纸本)9783031889912

Open linked data forms a crucial element at the core of Web 3.0, and a novel approach is proposed in this paper to address this need. The proposed model is designed to automate the process of data generation by extracting topics and categories from the dataset, followed by topic modelling and data synthesis through web crawling across the World Wide Web’s intricate structure. This synthesized data is subjected to classification using robust deep learning LSTM and SVM classifiers. The model incorporates various techniques such as Cosimrank and Belle Ahmed’s index, both integral to Imperialistic Competitive Algorithm, to compute semantic similarity and relevance, enhancing semantic orientation and metaheuristic optimization. Through metadata generation, additional localized knowledge is exponentially integrated, further enriching the model’s depth. The classification of metadata using the LSTM classifier enhances the model’s granularity. By incorporating Jaccard similarity and APMI measure, the model infers semantics-related connections effectively. Furthermore, the inclusion of E-book Glossary information significantly accelerates auxiliary knowledge integration, ultimately refining precision, sensor percentage, F-measure, and FDA of the framework. This innovative open-ended linked data generation paradigm seamlessly integrates deep learning for semantic orientation and reasoning, paving the way for a more advanced Web 3.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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On Consistent and Weak Transitive Neutrosophic Fuzzy Matrices

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Neutrosophic Sets and Systems 2025年 79卷 572-598页

作者： Radhika, K. Kavitha, M. Thirumalai, K. Geethanjaliyadav, R. Buvaneswari, N. Kamalakannan, V. Tamilnadu Thanjavur613005 India Department of Mathematics Panimalar Engineering College Tamilnadu Chennai India Department of Mathematics Saveetha Engineering College Tamilnadu Chennai602105 India Department of Mathematics Rajalakshmi institute of Technology Poonamalle Tamilnadu Chennai India Department of Mathematics Siga College of Management and Computer Science Villupuram Affiliated to Annamalai University Tamilnadu Chidambaram India

This paper delves into the properties of two specific types of Neutrosophic Fuzzy Matrices (NFM) namely consistent and weakly transitive NFM. A significant focus is placed on nilpotent and transitive NFM, highlighting their critical role in the analysis. It is demonstrated that these two types of matrices are controllable, and a formula is derived for determining the canonical form of a weakly transitive NFM. To support and clarify the findings, counterexamples are provided throughout the discussion. We introduce an operation on NFM, referred to as the Gödel implication operator. Utilizing this operator, we establish several significant results for NFM, with a particular emphasis on properties related to pre-orders. Our analysis focuses primarily on reflexive and transitive NFM, enabling us to derive meaningful insights. Additionally, we demonstrate a method for constructing an idempotent NFM from any given matrices. © 2025, University of New Mexico. All rights reserved.

关键词： Matrix algebra

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Ultra Wideband Frequency Selective Surface for Microwave Band Applications 9

Ultra Wideband Frequency Selective Surface for Microwave Ban...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Praveena, R. Babu, T.R. Ganesh Kirubakaran, J. Elango, S. Muthayammal Engineering College Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India

ISBN: (纸本)9798350384208

This paper is about creating and testing a special surface that can block specific frequencies of electromagnetic waves, and it's designed to work well regardless of the direction and polarization of the incoming waves. The unit cell of this FSS is composed of a square loop element combined with a crossed dipole featuring a circular groove element. Especially, the unit cell's size has been significantly reduced to 0.047 times the free-space wavelength (λ) corresponding to the lowest UWB frequency. The FSS in this work performs exceptionally well, as it has a bandwidth with a signal loss of -3dB that ranges from 3.05 GHz to 10.73 GHz. This bandwidth effectively covers the entire Ultra-Wideband (UWB) spectrum and the proposed FSS unit cell consists of a square-loop and a ring printed on opposite sides of a low-cost metamaterial substrate. Additionally, because of the small size of the individual unit cells in the FSS, it maintains its performance very well even when the electromagnetic waves hit it at angles as wide as 60 degrees, whether the waves have a perpendicular (TE) or parallel (TM) polarization. © 2023 IEEE.

关键词： Frequency selective surfaces

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BioSegNet: Pioneering Convolutional Approaches in Medical Imaging 5

BioSegNet: Pioneering Convolutional Approaches in Medical Im...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Swathika, R. Akshatha, P.S. Sheela, S. Dilip Kumar, S.M. Dr. Apj Abdul Kalam School of Engineering Garden City University Department of Engineering Bengaluru India New Horizon College of Engineering Department of Aiml Bengaluru India Siddaganga Institute of Technology Department of Computer Science Tumakuru India University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331528539

The integration of machine learning in biomedical imaging has significantly advanced the detection of tumors in brain using MRI scans. While traditional manual methods are effective, they often suffer from human error, inefficiency, and variability in diagnostic accuracy. BioSegNet addresses these challenges by introducing a CNN-based approach for automated detection of tumor in brain, segmentation, and classification of different types of tumors in MRI scans. This system reduces reliance on subjective interpretation and minimizes delays associ-ated with manual analysis. Built with TensorFlow Lite, BioSegNet ensures efficient deployment on edge devices and cloud platforms, making it versatile and accessible. Its user-friendly Streamlit-based web interface caters to users with varying technical expertise, enhancing its practical application in clinical settings. Extensive testing shows BioSegNet's ability to deliver accurate, real-time tumor analysis, offering a valuable tool for improving diagnostic accuracy and reducing errors. By optimizing for edge and cloud deployment, BioSegNet makes advanced diagnostic tools more accessible to medical professionals globally, contributing to better patient outcomes and timely treatments. © 2024 IEEE.

关键词： Ability testing

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Using NSCTS and DCNN, Rapid Prediction and Forecasting of Breast Cancer 1

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Br...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Seethalakshmi, K. Bhagyalakshmi, A. Sangeetha, A. Vijitha, S. Palukuri, P. Suhas Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350316469

Utilizing the CAD system, which aids radiologists in various diagnostic procedures, a recent study has been conducted on the early identification of breast cancer. Next to lung cancer, breast cancer ranks among the most deadly diseases. Initially, detection is done based on screening tests done through Magnetic resonance imaging (MRI), subsequently Infrared thermography, and digital mammography (DM). Whenever a suspicion is raised, these tests might be recommended by the specialist. Region-based segmentation is used for the test picture in this work, while NSCTS (Non-subsample Contourlet transform segmentation) is used for preprocessing. ResNet-164 DCNN architecture performs feature extraction and classification between normal and aberrant tumor pictures. The diagnosis system adopts the deep learning technology to increase its performance rate. The approach to prediction starts with screening test photos, then preprocessing to enhance the image quality, ROI detection, ROI segmentation, and classification. The resulting images are examined using a variety of evaluation measures, including performance rate, sensitivity, specificity, and accuracy. When compared to other recent work, the outcome is exceptional. The obtained accuracy, sensitivity of the proposed system is 98.58% and 98.78%. The application takes advantage of the MIAS mammography image dataset. © 2023 IEEE.

关键词： Breast cancer contourlet segmentation DCNN Digital mammograms Image preprocessing MRI ResNet

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Paradoxical roles of inhibitory autapse and excitatory synapse in formation of counterintuitive anticipated synchronization

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Chinese Physics B 2023年第8期32卷 499-509页

作者：丁学利古华光李玉叶贾雁兵 Department of Public Basic Education Fuyang Institute of TechnologyFuyang 236031China School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China College of Mathematics and Computer Science Chifeng UniversityChifeng 024000China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Different from the common delayed synchronization(DS)in which response appears after stimulation,anticipated synchronization(AS)in unidirectionally coupled neurons denotes a counterintuitive phenomenon in which response of the receiver neuron appears before stimulation of the sender neuron,showing an interesting function of brain to anticipate the *** dynamical mechanism for the AS remains unclear due to complex dynamics of inhibitory and excitatory *** this article,the paradoxical roles of excitatory synapse and inhibitory autapse in the formation of AS are ***,in addition to the common roles such that inhibitory modulation delays and excitatory modulation advances spike,paradoxical roles of excitatory stimulation to delay spike via type-II phase response and of inhibitory autapse to advance spike are obtained in suitable parameter regions,extending the dynamics and functions of the excitatory and inhibitory ***,AS is related to the paradoxical roles of the excitatory and inhibitory modulations,presenting deep understandings to the *** autapse induces spike of the receiver neuron advanced to appear before that of the sender neuron at first,and then excitatory synapse plays a delay role to prevent the spike further advanced,resulting in the AS as the advance and delay effects realize a dynamic ***,inhibitory autapse with strong advance,middle advance,and weak advance and delay effects induce phase drift(spike of the receiver neuron advances continuously),AS,and DS,respectively,presenting comprehensive relationships between AS and other *** results present potential measures to modulate AS related to brain function.

关键词： anticipated synchronization inhibitory autapse excitatory synapse paradoxical firing

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Detecting Objects in Surveillance Videos with Deep Neural Networks for Crime Scene Analysis

Detecting Objects in Surveillance Videos with Deep Neural Ne...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sudha, I. Kirubasri, G. Deivakani, M. Singh, Vijendra Pratap Kumar, T.Rajesh Niranjanamurthy, M. Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications U.P Varanasi India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Department of Ai and Ml Karnataka Bangalore India

ISBN: (纸本)9798350348910

Surveillance systems are critical components of modern security and forensic investigation, and their efficacy is strongly reliant on precise object detection in video recordings. This study looks into the use of deep neural networks to improve object detection in surveillance films for crime scene analysis. This study investigates the capabilities of cutting-edge deep learning architectures in recognizing and classifying objects in various surveillance contexts. The study's findings are extensive, including detection accuracy metrics for numerous item classes such as 'Person,' 'Vehicle,' and 'Suspicious Item.' Precision values range from 0.78 to 0.92, recall values range from 0.82 to 0.90, and F1 scores range from 0.80 to 0.90, demonstrating the models' ability to recognize objects accurately, but with variances among item categories. This study also looks into computational performance, offering information about inference times and GPU utilization. Inference times for ResNet-50 and YOLOv3 are 15 ms and 20 ms, respectively, with GPU use percentages of 75% and 90%. These findings provide useful information for picking models that fulfill real-time processing needs while optimizing computational resources. The research also examines the connection between video resolution, detection speed (up to 30 frames per second), and average detection accuracy. Lower resolutions allow for faster processing, but at the expense of accuracy, whilst higher resolutions provide finer details at the expense of larger computational needs. These trade-offs are critical considerations when building surveillance systems to meet certain operational requirements. © 2023 IEEE.

关键词： Object detection

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