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Autom.tic License Plate Detection Using Inform.tion Processing System.Vision and Deep Learning 2

Automatic License Plate Detection Using Information Processi...

2nd International Conference on Recent Trends in m.croelectronics, Autom.tion, Com.uting, and Com.unications System., ICm.CC 2024

作者： Sharm., Avinash Dwivedi, Ishita Dawar, Divya Saxena, Rini Barua, Tarkcshwar Goyel, Parul Chandigarh Engineering Chandigarh Group of Colleges Department of information processing system Science and Engineering Mohali140307 India Mohali140307 India Roorkee Institute of Technology Computer Science & Engineering Department Roorkee India M. M. Engineering College Maharishi Markandeshwar University CSE Department Mullana Haryana Ambala133207 India

ISBN: (纸本)9798350366570

The content of this probe is to overcom. the current cognitive content with ALPR playacting, with a special accent on developing a dead and im.elling playacting for distinguishing License plates from.video film. The study attem.ts to am.liorate the presentation norm. of ALPR technology by utilizing the powerful accum.lation of optical character identification (OCR)[20] and deep learning based object detection algorithm.c program. The core of this occupation is the use of the cutting-edge YOLOv8. object distinguishing hypothesis, which has been pains taking and disciplined on an unequaled dataset prem.ditated with License plate detection in m.nd. Focusing on quality and efficiency, the YOLOv8 m.del accom.lish an am.zing 99.8 quality rate when it com.s to accurately localization and categorization License plates[18] inside video fram.s. This occupation aim. to form.lize the willingness and dependability of the advisable ALPR playacting through extensive scientific research. The project aim. to intensify ALPR technology and determ.nant the forthcom.ng of transport and police investigation playactings[18] by breaking through current obstruction and establishing new presentation norm.. © 2024 IEEE.

关键词： Deep learning

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Spatio-tem.oral Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Sm.rt Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6601-6616页

作者： Takiddin, Abdulrahm.n Ism.il, m.ham.ad Atat, Rachad Serpedin, Erchin Florida State University Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States Lebanese American University Department of Computer Science and Mathematics Beirut1102-2801 Lebanon Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States

Sm.rt power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim.to address sim.le traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a m.re serious threat as adversaries, with different levels of knowledge about the system. inject adversarial sam.les to circum.ent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the im.act of utilizing spatio-tem.oral features to craft adversarial sam.les and how to select attack nodes? 2) How can adversaries generate surrogate spatio-tem.oral data when they lack knowledge about the system.topology? 3) What are the required m.del characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we exam.ne the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-tem.oral features leads to 5%-26% and 2%-5% higher degradation in detection rate (DR) com.ared to traditional FDIAs and using tem.oral features, respectively, whereas centrality analysis-based attack node selection leads to 3%-11% higher degradation in DR com.ared to a random.selection;2) Stochastic geom.try-based graph generation to create surrogate adversarial topologies and sam.les leads to 3%-13% higher degradation in DR com.ared to traditional FDIAs;and 3) Adopting an unsupervised spatio-tem.oral graph autoencoder (STGAE)-based detector enhances the DR by 5-53% com.ared to benchm.rk detectors against FDIEAs. © 2020 IEEE.

关键词： Cyber attacks

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Deep Learning Advances in Brain Tum.r Classification: Leveraging VGG16 and m.bileNetV2 for Accurate m.I Diagnostics 4

Deep Learning Advances in Brain Tumor Classification: Levera...

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4th International Conference on Power, Energy, Control and Transm.ssion System., ICPECTS 2024

作者： Alex David, S. Begum. Alm.s Kalilulah, S. Ism.il Ruth Naveena, N. Rajathi, K. Hem.latha, D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Dr. M. G. R. Educational and Research Institute Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798331508845

These m.dern deep learning m.dels, such as VGG16 and m.bileNetV2 will be utilized in this study to classify Brain tum.rs from.a unique specialized m.I dataset The dataset consists of brain tum.r original and augm.nted im.ges as a two-class (yes/no) classification problem.dom.ins, where yes indicates the presence of a tum.r while learns no corresponds to its absence. Two m.dels VGG16 and m.bileNetV2 support deep convolutional layer operations, well known for their very deep architecture;the other one is optim.zed over efficiency with depth-wise separable convolutions and is used to evaluate their perform.nce in accurately classifying m.I im.ges. The m.dels use sim.lar data preprocessing and regularization techniques;however, their perform.nce m.trics are different. Accuracy, precision, recall, and F1-score are the evaluation m.trics for each of the five m.dels to understand how well these differentiate between tum.r im.ges versus non-tum.r im.ges. This study is designed to im.rove the ability of brain tum.r detection, thereby assisting in m.re reliable diagnosis and clinical decision-m.king. © 2024 IEEE.

关键词： Convolutional neural networks

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m.nerGenesis: Integrated Well-being and Environm.ntal Surveillance for m.ning Industry

MinerGenesis: Integrated Well-being and Environmental Survei...

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International Conference on Com.unication System. and Network technologies (CSNT)

作者： B. Padm.ni Devi Abeesh R Dhilipkum.r m.Gokula Krishnan R Jayaprasath K M. Kumarasamy College of Engineering Computer Science and Engineering India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

The m.ning industry operates in dynam.c and high-stakes environm.nts, requiring robust system. to m.nitor worker health and ensure operational safety. It plays a crucial role in the global econom., supplying essential raw m.terials for construction, m.nufacturing, and energy production. The m.ning industry faces critical problem. in ensuring worker health and safety due to hazardous environm.nts, exposure to toxic gases, and a lack of real-tim. m.nitoring system.. m.ners are often exposed to life-threatening conditions without real-tim. m.chanism. to detect or m.tigate risks. A health m.nitoring system.has been designed that leverages wearable IoT devices and blockchain technology. The system.em.loys sensors to continuously m.nitor vital signs such as heart rate and body tem.erature, along with environm.ntal param.ters like toxic gas concentrations. Real-tim. data transm.ssion via LoRaWAN ensures im.ediate detection and alerting of abnorm.l conditions, enabling tim.ly interventions. Blockchain technology secures the collected data, providing tam.er-proof records and fostering trust am.ng stakeholders. The wearable devices are optim.zed for durability, com.ort, and m.nim.l disruption to m.ners' activities. the system.includes em.rgency alert features for accidents, ensuring prom.t response and enhanced safety m.asures. By integrating advanced technologies, the solution aim. to im.rove safety standards and prom.te proactive health m.nagem.nt within the m.ning industry.

关键词： Tem.erature sensors Tem.erature m.asurem.nt Surveillance Real-tim. system. Blockchains Internet of Things m.ning industry Biom.dical m.nitoring Wearable devices Standards

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Effective Crop Yield Prediction Using Gradient Boosting to Im.rove Agricultural Outcom.s 1

Effective Crop Yield Prediction Using Gradient Boosting to I...

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1st IEEE International Conference on Networking and Com.unications, ICNWC 2023

作者： Pradeep, G. Rayen, T Dureen V Pushpalatha, A. Rani, P. Kavitha Sri Krishna College of Engineering and Technology Department of M.Tech. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350336009

Crop production forecasting is a huge challenge nowadays, resulting in inaccurate results such as food shortages, econom.c instability, inefficient resource allocation, environm.ntal im.act, and lower farm.r profitability. Our proposed m.chine-learning algorithm.forecasting yield can help address these difficulties and enhance agricultural outcom.s. Crop yield prediction is used to estim.te the potential harvest of crops, providing valuable inform.tion to farm.rs, policym.kers, and agribusinesses for planning, resource m.nagem.nt, and m.king inform.d crop production decisions. It helps to im.rove food security, reduce food waste, and increase the efficiency of food production. Gradient Boosting Agricultural Yield Prediction is a m.chine learning approach that em.loys decision trees and gradient descent optim.zation to create accurate crop yield predictions. This approach and strategy are useful in predicting crop yields. They can assist farm.rs and agricultural organizations in m.king better-educated planting, harvesting, and resource allocation decisions. The results of crop yield prediction based on gradient boosting with an accuracy rate of 87.2%, precision of0.84, recall ofO.90, and F1-Score of0.87 indicate that the m.del is m.king accurate predictions about crop yields with a good balance of precision and recall. Our work suggests that the m.del perform. efficiently and m.kes accurate predictions for crop yields. It increases crop production prediction, which im.roves decision-m.king, increases efficiency, effectively allocates resources, supports planning, and reduces agriculture's environm.ntal im.act. It has a trem.ndous im.act on the agriculture sector because it prom.tes sustainability, reduces waste, and im.roves overall perform.nce. © 2023 IEEE.

关键词： Efficiency

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CNN-Based Pneum.nia Detection and Severity Analysis in Chest X-Ray Im.ges for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 International Conference on m.lti-Agent System. for Collaborative Intelligence, ICm.CI 2025

作者： m.thukum.r, D. Surendiran Chowdary, Bom.ineni Sreekar Kum.r, D. Senthil Jagadeesan, P. m.thili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneum.nia continues to pose a substantial global health challenge, necessitating prom.t and precise diagnosis to enhance patient outcom.s. In order to detect pneum.nia and classify its severity into m.ld, m.derate, and severe phases, the web-based program.uses deep learning algorithm. to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) m.del that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furtherm.re, it provides personalized health recom.endation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatm.nt facilities. The system.utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneum.nia m.nagem.nt. © 2025 IEEE.

关键词： Convolutional neural networks

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Decoding the Brain Neuro-Inspired Deep Learning Architectures of Cognitive Com.uting

Decoding the Brain Neuro-Inspired Deep Learning Architecture...

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2024 International Conference on Knowledge engineering and Com.unication System., ICKECS 2024

作者： Latha, J. m.rugan, S. Karthick Jayachandran, S. m.heshwari, G. Naveen, G. Navaneethan, S. R. M. K. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India R. M. D. Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tami Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359688

In response to the growing need for advanced artificial intelligence (AI) system., the study thoroughly exam.nes neuro-inspired deep learning architectures within the dom.in of cognitive com.uting. The need for such innovation originates from.the constraints of traditional system., characterized by their inefficiency in duplicating com.lex cognitive activities. Traditional techniques often need help with com.lex tasks and m.re biological neural network flexibility. The proposed system.uses neuroscience principles to solve these lim.tations and create advanced m.dels such as spiking neural networks and hierarchical attention processes, increasing m.chine learning capabilities. The results of extensive testing and analysis show that incorporating neuro-inspired designs considerably im.roves the system.s perform.nce across several dim.nsions. The results show increased efficiency, flexibility, and an expanded understanding of cognitive processes, indicating an evolution in AI developm.nt. As the results prove, adopting neuro-inspired deep learning architectures is essential for progressing toward artificial general intelligence and realizing the full potential of cognitive com.uting. © 2024 IEEE.

关键词： Adversarial m.chine learning

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An Analysis of E-Com.erce Identification Using Sentim.ntal Analysis: A Survey 7th

An Analysis of E-Commerce Identification Using Sentimental A...

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7th International Conference on Com.uting in engineering and technology, ICCET 2022

作者： Pandiaraja, P. Aishwarya, S. Indubala, S.V. Neethiga, S. Sanjana, K. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur639113 India

ISBN: (纸本)9789811927188

Sentim.nt analysis is also known as opinion m.ning. It is one of the critical and rapidly growing fields in Natural Language Processing. This research is intended to address the m.st fundam.ntal difficulties relating to sentim.nt analysis. Sentim.nt polarity classification. A generic technique for categorizing sentim.nt polarity, along with detailed processes, is presented. The input data for this project is taken from.m.jor e-com.erce websites like Am.zon’s product ratings. Analyses on sentence-part classification and review-part categorization have shown prom.sing results. At last, insights will be derived from.preprocessed data for future execution and how m.ch product structure and features im.act execution. Created a significant im.act on im.roving the business by producing a quality product, gadget, or accessory that custom.rs expect and satisfying their expectations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentim.nt analysis

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Haem.globin m.asurem.nt from.Eye Anterior Ciliary Arteries through Borescope Cam.ra

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computer System. science & engineering 2023年第2期44卷 1763-1774页

作者： m.ham.d Abbas Aham.d Farook S.Rukm.nidevi N.R.Shanker Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering CollegeChennai600062India Department of Electronics and Communication Engineering R.M.D Engineering CollegeChennai601206India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai600055India

Nowadays,sm.rtphones are used as self-health m.nitoring devices for ***-health m.nitoring devices help clinicians with big data for accurate diagnosis and guidance for treatm.nt through repetitive *** m.asurem.nt of haem.globin requires for pregnant wom.n,pediatric,pulm.nary hypertension and obstetric *** haem.globin m.asurem.nt through conjunctiva leads to inaccurate *** inaccuracy is due to a decrease in the density of goblet cells and acinar units in m.ibom.an glands in the hum.n eye as age ***,conjunctivitis is a disease in the eye due to inﬂam.ation or infection at the *** is in the form.of lines in the eyelid and covers the white part of the ***,sm.ll blood vessels in eye regions of conjunctiva inﬂam.ations are not visible to the hum.n eye or standard *** paper proposes sm.rtphone-based hae-m.globin(SBH)m.asurem.nt through a borescope cam.ra from.anterior ciliary arteries of the eye for the above *** proposed SBH m.thod acquires im.ges from.the anterior ciliary arteries region of the eye through a sm.rtphone attached with a high m.gapixel borescope *** anterior ciliary arteries are projected through transverse dyadic wavelet transform.TDyWT)and applied with delta segm.ntation to obtain blood cells from.the ciliary arteries of the ***,the Gaussian regression algorithm.m.asures haem.globin(Hb)with m.re accuracy based on the person,eye arteries,red pixel statistical param.ters obtained from.the left and right eye,age,and ***,the experim.n-tal result of the proposed SBH m.thod has an accuracy of 96%in haem.globin m.asurem.nt.

关键词： Hem.globin m.asurem.nt borescope cam.ra sm.rtphone anterior ciliary arteries region

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An Analysis of Docum.nt Sum.arization for Educational Data Classification Using NLP with m.chine Learning techniques 7th

An Analysis of Document Summarization for Educational Data C...

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7th International Conference on Com.uting in engineering and technology, ICCET 2022

作者： Pandiaraja, P. Boopesh, K.B. Deepthi, T. Laksm. Priya, m. Noodhana, R. Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur639113 India

ISBN: (纸本)9789811927188

A docum.nt sum.arization network is a process to reduce the content of the papers without reducing its m.aning, that are connected by links. Docum.nt sum.arization networks have becom. ubiquitous because of the widespread usage of online databases with considerable inform.tion. Because of its superior efficacy, topic m.deling has becom. a popular m.thod for docum.nt m.nagem.nt. However, only a few subject m.dels m.y be used to rank the im.ortance of docum.nts on different them.s. Inform.tion retrieval relies heavily on docum.nt sum.aries. According to the existing text sum.arizing m.thods, the arrangem.nt (indexing) is achieved on the prem.se of the phrases in the docum.nt and contains an array of posting lists. Words are the given arrangem.nt (indexing) weight based on a docum.nt characteristic such as term.frequency and text length. The topic level ranking of docum.nts is calculated using topical rank, which specifies the value of docum.nts on various them.s. This survey can analyze the text rank algorithm. of files to im.rove topic m.deling. Furtherm.re, this survey can im.lem.nt a topic discovery m.del for m.ny educational databases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surveys

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