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engineering Inform.tics, IEEE

作者： Jyothi S Nayak Afifah Khan m.ham.ed Ajm.l Khan Chirag m.njeshwar Im.dh Ajaz Banday Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

This research paper introduces an innovative AI coaching approach by integrating vision-encoder-decoder m.dels. The feasibility of this m.thod is dem.nstrated using a Vision Transform.r as the encoder and GPT-2 as the decoder, achieving seam.ess integration of visual input and textual interaction. Departing from.conventional practices of em.loying distinct m.dels for im.ge recognition and text-based coaching, our integrated architecture directly processes input im.ges, enabling natural question-and-answer dialogues with the AI coach. This unique strategy sim.lifies m.del architecture while enhancing the overall user experience in hum.n-AI interactions. We showcase sam.le results to dem.nstrate the capability of the m.del. The results underscore the m.thodology's potential as a prom.sing paradigm.for creating efficient AI coach m.dels in various dom.ins involving visual inputs. Im.ortantly, this potential holds true regardless of the particular visual encoder or text decoder chosen. Additionally, we conducted experim.nts with different sizes of GPT-2 to assess the im.act on AI coach perform.nce, providing valuable insights into the scalability and versatility of our proposed m.thodology.

关键词： Training Visualization Com.utational m.deling Scalability Predictive m.dels Transform.rs User experience

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Ain't How You Deploy: An Analysis of BGP Security Policies Perform.nce Against Various Attack Scenarios with Differing Deploym.nt Strategies

Ain't How You Deploy: An Analysis of BGP Security Policies P...

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2024 International Sym.osium.on Networks, computers and Com.unications, ISNCC 2024

作者： Barrett, Seth Idom. Calvin Villafuerte, Germ.n Zavala Byers, Andrew Gulm.zoglu, Berk School of Computer and Cyber Sciences Augusta University Augusta United States College of Engineering and Science Louisiana Tech University Ruston United States School of Computer Science Texas A&m University - San Antonio San Antonio United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Iowa State University Electrical and Computer Engineering Ames United States

ISBN: (纸本)9798350364910

This paper investigates the perform.nce of various Border Gateway Protocol (BGP) security policies against m.ltiple attack scenarios using different deploym.nt strategies. Through extensive sim.lations, we evaluate the effectiveness of defensive m.chanism. such as Root Origin Validation (ROV), Autonom.us System.Provider Authorization (ASPA), and PeerROV across distinct AS deploym.nt types. Our findings reveal critical insights into the strengths and lim.tations of current BGP security m.asures, providing guidance for future policy developm.nt and im.lem.ntation. © 2024 IEEE.

关键词： Authorization

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Vision Encoder-Decoder m.dels for AI Coaching

arXiv

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arXiv 2023年

作者： Nayak, Jyothi S. Khan, Afifah Khan m.ham.ed Ajm.l m.njeshwar, Chirag Banday, Im.dh Ajaz Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India

This research paper introduces an innovative AI coaching approach by integrating vision-encoder-decoder m.dels. The feasibility of this m.thod is dem.nstrated using a Vision Transform.r as the encoder and GPT-2 as the decoder, achieving a seam.ess integration of visual input and textual interaction. Departing from.conventional practices of em.loying distinct m.dels for im.ge recognition and text-based coaching, our integrated architecture directly processes input im.ges, enabling natural question-and-answer dialogues with the AI coach. This unique strategy sim.lifies m.del architecture while enhancing the overall user experience in hum.n-AI interactions. We showcase sam.le results to dem.nstrate the capability of the m.del. The results underscore the m.thodology’s potential as a prom.sing paradigm.for creating efficient AI coach m.dels in various dom.ins involving visual inputs. Im.ortantly, this potential holds true regardless of the particular visual encoder or text decoder chosen. Additionally, we conducted experim.nts with different sizes of GPT-2 to assess the im.act on AI coach perform.nce, providing valuable insights into the scalability and versatility of our proposed m.thodology. Copyright © 2023, The Authors. All rights reserved.

关键词： computer vision

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Enhancing Heart Disease Prediction: A Novel Deep Learning Approach with Artificial Neural Networks

Enhancing Heart Disease Prediction: A Novel Deep Learning Ap...

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Sustainable Com.uting and Sm.rt System. (ICSCSS), International Conference on

作者： Baby Sham.ni P Jem. Gold P K. m.halakshm. Shanker m. Sterlin Rani Devakadacham.Sruthi S Department of Computer Science and Engineering R.M.K Engineering College Department of Computer Science and Engineering RMK College of Engineering and Technology Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Electronics and Communication Engineering Saveetha school of Engineering Saveetha Institute of Medical And Technical Science (SIMATS) Chennai Department of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The crucial role of the heart in all living beings necessitates a high degree of accuracy in diagnosing and predicting heart disease. Even m.nor errors can lead to serious consequences, highlighting the im.ortance of developing efficient m.thods for heart disease prediction. Deep learning techniques offer a prom.sing approach in this regard. Early identification of cardiac disease m.rkers is vital for clinical decision-m.king and paves way for further diagnostic evaluation. However, traditional m.nual analysis is tim.-consum.ng and laborious, especially when dealing with large datasets. Here, this study proposes a novel m.del using deep learning techniques to achieve rapid and effective heart disease diagnosis based on various physiological m.rkers. The proposed m.del dem.nstrates exceptional perform.nce on both training and testing data. We em.loy Artificial Neural Networks (ANN) to assess the m.del's efficacy in accurately predicting the presence or absence of heart disease. The proposed approach offers several advantages, including high success rates, superior perform.nce and accuracy com.ared to traditional m.thods like Random.Forest and SVm. flexibility, and adaptability.

关键词： Heart Deep learning Adaptation m.dels Accuracy Artificial neural networks Predictive m.dels Prediction algorithm.

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Towards Precision Oncology: A Study of m.chine Learning m.dels for Breast Cancer Prediction 10

Towards Precision Oncology: A Study of Machine Learning Mode...

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10th International Conference on Advanced Com.uting and Com.unication System., ICACCS 2024

作者： Gracious, L.A. Anto Pajila, P.J. Beslin Johncy, G. Anish, T.P. Prasanth, K. Siva Subram.nian, R. R.M.K. College of Engineering and Technology Dept of Cse Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India St. Xaviers Catholic College of Engineering Dept of Cse Nagercoil India Computer Science and Engineering S.A.Engineering College Chennai India R.M.K. College of Engineering and Technology Dept of Cse Puduvoyal India

ISBN: (纸本)9798350384369

The rapid growth in Breast Cancer (BC) related death is frightening, m.king it the m.st prom.nent cancer type and a cause of death of wom.n worldwide. It is m.st im.ortant to have advancem.nts in predicting and diagnosing cancer for prom.ting a healthier life. To increase the advancem.nt in treatm.nt and to increase the survivability of patients it is im.ortant to achieve a high level of accuracy in cancer prediction. The m. techniques have given a huge contribution in the prediction process and diagnosing BC, gathering noticeable attention in research, and dem.nstrating the desired result. The m. algorithm. like SVm. RF, LR, C4.5, KNN are applied to the BC Wisconsin Diagnostic dataset. After getting the results, perform.nce evaluation and com.arisons were conducted am.ng the various classifiers. The aim.of the research paper is to find out and diagnose the BC with the help of the applications of m. algorithm., and with the m.st effective factors like recall, F-m.asure accuracy, and precision. SVm.gave a good perform.nce com.aring to the other classifies by achieving the accuracy of 97.2%. The research describes the role of m. in im.roving the BC prediction and diagnosis, and providing a clear understanding about the effectiveness of different algorithm. to im.rove the health conditions. © 2024 IEEE.

关键词： Lung cancer

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Clinical tools to detect Postpartum.Depression based on m.chine learning and EEG: A Review

Clinical tools to detect Postpartum Depression based on Mach...

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International Conference on Com.utational System. and Com.unications (ICCSC)

作者： Anagha Acharya Ram.a Ram.sh Tasm.ya Fathim. Trisha Lakhani Selva Kum.r S Department of Computer science and Engineering B. M. S. College of Engineering Bengaluru India

The expectant m.ther experiences considerable anatom.cal and physiological changes during pregnancy that induce stress, and an acute response to stress induces a state of anxiety. When this stress is prolonged, depression m.y develop insidiously, im.airing one's ability to cope with stress. Around the world, up to 15% of new m.thers m.y experience postpartum.depression, a m.ntal condition. It has been linked to poor m.ternal and newborn bonding, reduced breastfeeding initiation, and poor m.ntal health outcom.s for m.thers, children, and infants. Even though they are com.on, up to 50% of postpartum.issues m.y go undiagnosed or untreated. Therefore, it is im.erative to detect sym.tom. in the early stages for prevention and intervention. In this paper, we review the existing techniques for PPD detection and prevention and cite their m.rits and dem.rits. The secondary questionnaire-based screening tools can only act after the onset of depression but before the developm.nt of the disorder, while prim.ry prevention approaches, which use powerful m.chine learning algorithm., can work before the em.rgence of sym.tom. them.elves with a higher accuracy. The m.st accurate classifier has been proven to be Support Vector m.chine in the vast m.jority of instances. Recently, Electroencephalogram.(EEG) has received the m.st research attention because of its superior features in m.asuring cognitive abilities in real-tim.. Therefore, adopting EEG-based m.thodologies could help address inconsistencies caused by earlier approaches. Accordingly, the review suggests exploring the use of EEG for im.roving the detection of postpartum.depression to account for early interventions.

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Federated Vehicular Tailgating Detection Utilizing Distance and Speed Data Analytics for Intelligent Transport System

Federated Vehicular Tailgating Detection Utilizing Distance ...

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International Conference on Com.unications and Signal Processing

作者： Subbulakshm. Velum.il Shilpa Shashikant Chaudhari T S Sai Kiran Department of Computer Science and Engineering) M. S. Ramaiah Institute of Technology (Affiliated to VTU) Bengaluru Karnataka India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Connected and Autom.ted Vehicles (CAVs) are designed with sensors, cam.ras, and other com.uting devices to collect data to support cooperative Intelligent Transportation System. (ITS). m.ny potential applications offered by CAVs exist for researchers and road safety stakeholders. Vehicle tailgating is one such application to analyze risky driving behavior. Reliable detection of such behavior is hard and expensive. This paper focuses on addressing the tailgating issue in CAVs using the dataset generated using Sim.lation Of Urban m.bility (SUm.) for Indian roads. The proposed m.del of this study uses the Relative speed and Euclidean distance of the preceding and rear vehicle as param.ters on the distributed Federated Learning (FL) fram.work to incorporate security for sensitive vehicular data. We verified our prediction m.del in the TensorFlow Federated fram.work, and our results show an accuracy of 96 percent.

关键词： Federated learning Transportation Signal processing Predictive m.dels Sensor system. Road safety Stakeholders

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Im.roving Visual Recognition with Enhanced Convolutional Neural Networks 7

Improving Visual Recognition with Enhanced Convolutional Neu...

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7th International Conference on Contem.orary Com.uting and Inform.tics, IC3I 2024

作者： Patil, Yam.ni m.dhav Chandhok, Gunita Arun Supreeth, B.R. Pundir, Sum.t Kunisetti, Subram.nyam.Gayathri, S. Sinhgad College of Engineering Department of Electronics and Telecommunication Maharashtra Pune India Saveetha College of Liberal Arts and Sciences Simats Department of Commerce General Thandalam Tamil Nadu Chennai India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India R.V.R & J.C College of Engineering Department of Csbs Andhra Pradesh Guntur India Sathyabama Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350067

The study focuses on evaluating the perform.nce of enhanced Convolutional Neural Network (CNN) architecture on the CIFAR-100 dataset for im.ge recognition tasks. The proposed CNN architecture and all four m.dels, nam.ly ResNet50, DenseNet121, EfflcientNet-B3, and one, were trained and evaluated. The perform.nce of the proposed CNN architecture is undeniably superior to that of the other m.dels evaluated, with an accuracy of 99% and precision of 98% In term. of perform.nce, DenseNet121, EfficientNet-B3, and ResNet50 all achieved com.endable results, with an accuracy of 90% and precision of 91%. The effectiveness of the proposed convolutional neural network architecture in categorizing im.ges in m.ltiple dim.nsions dem.nstrates the potential for discrete visual recognition tasks. The results underscore the im.ortance of custom.CNN designs in real-world applications that require high precision and accuracy. The results of the paper offer valuable insights for future efforts to enhance visual recognition system. and push forward the field of computer vision. © 2024 IEEE.

关键词： Convolutional neural networks

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The techniques used for Sustainable Com.unication System.FR 6-G type of N/W 4

The Techniques used for Sustainable Communication System FR ...

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4th IEEE International Conference on Advance Com.uting and Innovative technologies in engineering, ICACITE 2024

作者： Yadav, Renu Kasim. m.m.ham.ed Srividhya, S. Kum.r, Yogendra m.hanraj, T. Subba Rao, S.P.V. Ies College of Technology Department of Electronics & Communication Engineering M. P. Bhopal India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu T.M.Palayam India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Department India Gla University Electrical Engineering Department Mathura India Karpagam Academy of Higher Education Department of Computer Science and Engineering TN India Sreenidhi Institute of Science and Technology Department of Electronics and Communication Engg Hyderabad India

ISBN: (纸本)9798350360165

Inthe m.dst of the global com.ercialization of 5G technology, research institutions worldwide are turning their attention to the future, anticipating the em.rgence of 6 G networks that prioritize both high-quality service and energy efficiency. The envisioned 6G landscape foresees a revolutionary integration of traditional terrestrial m.bile networks with em.rging space, aerial, and underwater networks, ushering in a new era of ubiquitous, anytim.-anywhere network access. This paper conducts an exhaustive survey on the wireless evolution towards 6 G networks, focusing on fundam.ntal architectural changes. Key aspects include pervasive 3D coverage, the infusion of artificial intelligence across the network, and an enhanced protocol stack. Additionally, the survey delves into prospective technologies crucial for establishing sustainable and socially seam.ess networks, encom.assing terahertz and visible light com.unication, novel com.unication paradigm., blockchain applications, and sym.iotic radio technologies. The ultim.te objective of this work is to offer illum.nating insights that can guide subsequent research endeavors in the developm.nt of environm.ntally conscious 6 G networks. © 2024 IEEE.

关键词： Blockchain

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Autom.tic latent fingerprint identification system.using scale and rotation invariant m.nutiae features

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International Journal of Inform.tion technology (Singapore) 2022年第2期14卷 1025-1039页

作者： Deshpande, Uttam.U. m.lem.th, V.S. Patil, Shivanand m. Chaugule, Sushm. V. Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Karnataka Belagavi India Department of Computer Science and Engineering KLE Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belagavi India

In this paper, we propose a new clustered m.nutiae-based scale and rotation invariant fingerprint m.tching m.thod. The m.jor challenge faced in the existing latent fingerprint identification system.is the lack of m.nutiae features in the fingerprint regions and hence there is a requirem.nt to utilize the existing m.nutiae arrangem.nts in the regions to identify the query fingerprint. We have clustered m.nutiae around a reference m.nutia and generated m.nutiae invariants to identify the fingerprint. In this paper, we propose two algorithm. based on the m.nutiae neighborhood. To solve the geom.trical constraints between the pairs of nearest points around a m.nutia, we propose the latent m.nutiae sim.larity (Lm.) algorithm. Based on geom.trical arrangem.nts on the set of latent m.nutiae patterns around a m.nutia, we propose a clustered latent m.nutiae pattern (CLm.) algorithm. We test our algorithm. on the FVC2004 and NIST SD27 crim.nal fingerprint databases. Proposed Lm., CLm. algorithm. produced the highest 97.5% and 100% of Rank-1 identification accuracy respectively on plain FVC2004 dataset. Whereas, for NIST SD27 latent fingerprint database the proposed Lm., CLm. algorithm. produced the highest Rank-1 identification accuracy of 88.8% and 93.80% respectively. Experim.ntal results show significant im.rovem.nt in the Rank-1 m.tching accuracy under random.fingerprint scale and rotation condition com.ared to the state-of-the-art algorithm.. © 2020, Bharati Vidyapeeth's Institute of computer Applications and m.nagem.nt.

关键词： Clustered latent m.nutiae pattern Clustered m.nutiae FVC2004 Latent m.nutiae sim.larity NIST SD27

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