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检索条件"机构=Computer Science And Engineering V.S.B Engineering College"

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A survey on deep Learning's Effectiveness in Detecting brain Tumors

A survey on deep Learning's Effectiveness in Detecting Brain...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Pothreddypally Jhansi Devi C. Pretty Diana Cyril Ch. Madhu babu Department of ComputingTechnologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computer Science Engineering B V Raju Institute of Technology Narsapur Medak Telangana India

Recognizing brain tumors is a crucial part of medical imaging. Deep learning algorithms have become effective tools for examining medical images. The present article seeks to give a thorough summary of current developments in deep learning-based brain tumor diagnosis. The investigation starts out by outlining the basic ideas of brain tumour identification and emphasizing its importance in tumor field of medicine. The survey emphasizes the necessity for larger and more varied datasets, the efficiency of automatic feature learning, as well as the significance of pre-processing and data augmentation in enhancing model performance. This investigation draws to a close with recommendations for future research areas and a focus on how deep learning has the ability to revolutionise the way that brain tumour detection is done while also enhancing patient outcomes.

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Raspberry pi based secured cloud data 1

Raspberry pi based secured cloud data

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1st International Conference on Advances in Computational science and engineering, ICACsE 2020

作者： Prasath Kumar, s. Rayavel, P. Anbarasi, N. Renukadevi, b. Maalini, D. Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Tamilnadu Chennai600044 India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Tamilnadu Chennai600044 India Department of Information Technology Anna University CEG Campus Tamilnadu India Department of Information Technology Sri Sai Ram Engineering College Tamilnadu Chennai600044 India Department of Information Technology V.S.B Engineering College Tamilnadu Karur India

In the growing digital world, Cloud storage plays a major role. Cloud storage is preferred for storing enormous amounts of data that our system cannot hold. but the cost of using private Cloud storage services will be costly and our data is under the control of others. The main objective of our idea is that our data will be within ourselves rather than sending the data to unknown Cloud storage providers and which is also cost effective. We are using the hard disks as our own Cloud storage which will be controlled by raspberry pi and it can be accessed from anywhere. It is also more secure of using the material cloud with ourselves rather than depending on a private Cloud storage service which may or may not be secure but here our data is only with ourselves as we will implement our suitable security measures. The design of our project is also much compact and portable as it gives a major advantage, we can carry it anywhere. The main motto of our project is to give a user personal Cloud storage of his own in a cost-efficient manner which will be much more reliable than any other material cloud services. © Published under licence by IOP Publishing Ltd.

关键词： Cloud storage

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Enhancing Predictive Model Performance through Comprehensive Pre-processing and Hybrid Feature selection: A study using svM 2

Enhancing Predictive Model Performance through Comprehensive...

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2nd International Conference on self sustainable Artificial Intelligence systems, ICssAs 2024

作者： Thuraka, bharadwaj Pasupuleti, vikram Kodete, Chandra shikhi Naidu, U. Ganesh Tirumanadham, N. s. Koti Mani Kumar shariff, vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States B V Raju Institute of Technology Department of Csbs Narsapur Telangana Medak India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350368413

This study aims to achieve the improved performance of heart disease prediction based on the increased level of preprocessing and rather effective feature selection approaches. starting with data preparation in which the data is first cleaned up the work develops a class balance solution using synthetic Minority Over-sampling Technique (sMOTE) while the Z-score function identifies outliers. As it will be described in CHAN section that applies Chi-square and ANOvA under a hybrid model, the useful characteristics for model training are identified and prioritized. Using a method derived from the medical records database, the study focuses on the heart health indices using support vector Machines (svM) which is a modelling technique widely known for its effectiveness in handling increased numbers of variables and the relationship between the measurements. Measures such as accuracy, precision, recall and F1-score among others are used in the evaluation of the success of the approach. In particular, the results shed the light to Health Care applications where risk assessment and correct diagnosis is extremely critical;the findings also assert the important roles of strong preprocessing and selective feature selection strategies in enhancing the predictive model's performance. This work contributes to the development of approaches intended for stable and efficient modelling in medical decision support system. © 2024 IEEE.

关键词： support vector machines

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vision based umpire detection method for event extraction in cricket video based on HOG and color image segmentation 1

Vision based umpire detection method for event extraction in...

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1st IEEE International Conference on Advances in Electrical, Computing, Communications and sustainable Technologies, ICAECT 2021

作者： Nandyal, suvarna Kattimani, suvarna L. Department of Computer Science and Engineering P. D. A. College of Engineering Karnataka Kalaburgi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G.Halakatti College of Engg. and Tech. Karnataka Vijayapur586103 India

ISBN: (纸本)9781728157900

As of late, there has been expanded interest in video summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in south Asian Countries. In cricket sport video, Umpire has the force for settling on huge choices about functions in the field. In this work, the manually chosen Recorded video of some International Twenty-Twenty cricket match video (T20*20) is used as dataset for Umpire identification inside the sport of cricket. The proposed dataset is assessed as a starter help for creating frameworks to consequently produce cricket features. With the growing increase of the utilization of technology in sports, this paper points basically at the Umpire Identification utilizing Area-Of-interest based Histogram of Oriented Gradients (AOI-HOG) Feature Extraction strategy for Human Identification. It expects to give Color Image segmentation technique utilizing pixel wise color information to support vector machine (svM) classification for Umpire Identification while protecting the arrangement of highlights. Test proof shows that the proposed technique accomplishes the Accuracy of 94.57%. Proposed one efforts towards successful segmentation results and computational conduct, and lessens the time and expands the norm of color image segmentation. © 2021 IEEE

关键词： support vector machines

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Adaptive sMC for modified three phase bridgeless vienna rectifier for Electric vehicle Applications

Adaptive SMC for modified three phase bridgeless Vienna rect...

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International Conference on Power, Energy, Control and Transmission systems (ICPECTs)

作者： G. Kannayeram R. saravanan P. Arulkumar G. venkatesan Department of Electrical and Electronics Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Balaji Institute of Technology and Science Warangal Telangana India Department of Electrical and Electronics Engineering V.S.B.Engineering College Karur Tamilnadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

This paper presents the three phase modified bridgeless vienna rectifier for electric vehicle (Ev) charger. This topology has significant impact on power quality improvement such as power factor improvement. The modified topology is more advantageous in automotive industry. The Ev charger developed using this technique is compact and economical. The DC bus voltage controlled in the designed vienna rectifier is achieved through adaptive sliding mode controller (sMC) controller. The adaptive sMC controller is designed by incorporating the characteristics of internal model controller (IMC). This DC voltage control enables the control on charging of Ev and battery. This adaptive sMC controller enhances the sinusoidal tracking performance and also reduces the steady state error. The proposed adaptive sMC controller also ensures the stability of the system. The proposed system is verified in simulation and the results are validated through comparison with sMC controller. The results are validated through power factor improvement in modified sMC.

关键词： Reactive power Adaptation models Power quality Rectifiers Load management Electric vehicle charging Topology steady-state voltage control Load modeling

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sentiment Analysis of Depression and Anxiety social Media Tweets Using TF-IDF Weighting and supervised Learning Algorithm

Sentiment Analysis of Depression and Anxiety Social Media Tw...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Neetesh Kumar Nema vivek shukla Amit Pimpalkar s. R. Tandan Dr. C. V. Raman University Bilaspur C.G India Department of Computer Science and Engineering Dr. C. V. Raman University Bilaspur C.G India Department of Computer Science and Engineering (AIML) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science Government R. V. R. S. Kanya Mahavidyalaya Kawardha C.G India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

One of the most prevalent mental illnesses and a significant contributing factor to suicides is depression. The increased usage of social media platforms has made it possible to diagnose depression early on by speaking to individuals in their native tongue. This research explores sentiment analysis on a Twitter dataset by categorizing tweets into ‘positive’ and ‘negative’ sentiments. The proposed model exhibits exceptional performance in the context of depression and anxiety sentiment classification. Employing pre-processing techniques enhanced dataset quality, including tokenization, lemmatization, and stop word removal. The entire process, including the analysis and sorting steps, would use social media data, including text data and emoticons, emojis, and other graphics. Incorporating the TF-IDF weighting scheme further refined sentiment conveyance by emphasizing specific terms. A supervised machine-learning algorithm was applied, and the proposed model demonstrated superior performance, achieving precision, recall, F1-score, and accuracy at 98.27%, 98.07%, 98.00%, and 98.01%, respectively. These remarkable metrics underscore the proposed model’s exceptional ability to classify tweets accurately, highlighting its robustness in handling X (formally Twitter) data distinctions for advanced social media analytics.

关键词： sentiment analysis Analytical models Accuracy social networking (online) blogs Anxiety disorders Depression

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Quantized Wavelet Dilated Convolutional Neural Network based Intelligent surveillance system for Public safety in IoT security Networks

Quantized Wavelet Dilated Convolutional Neural Network Based...

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sustainable Expert systems (ICsEs), International Conference on

作者： R. sowmya Ruchira Rawat Dinesh Mishra Mamtaben Kamleshbhai Megha Monikaben Maheshbhai Patel sathish Kannan Department of CSE (AIML) S. A. Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Mangalayatan University-Jabalpur Jabalpur Madhya Pradesh India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Anand Gujarat India Department of Computer Science Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of CVM University Anand Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

smart cities have produced vast amounts of surveillance video data that can be processed to identify anomalies since the introduction of visual sensors. A novel Quantized Wavelet Auxiliary Encoder self-Attention Dilated Convolutional Neural Network with Gold Rush Optimizer (QWAEsDCNN-GRO) designed to enhance security and reduce crime potential. Through the integration of the Quantized Discrete Haar Wavelet Transform (QDHWT), frame-level features have been extracted. subsequently, the recovered features are fed to the Auxiliary Guided Knowledge Encoder-Decoder (AGED), which uses them to rank the features. These features are then sent to the self-Attention Causal Dilated Convolutional Neural Network (sACDCNN) with Gold Rush Optimization (GRO) for anomaly detection. Performance indicators including Fl-score, recall, accuracy, precision, and AUC are compared with those of earlier techniques in order to assess the effectiveness of the recommended strategy. Using the UCF-Crime dataset, the model achieved an accuracy of 99.87% and an error rate of 0.021%. On the Hockey Fights dataset, it obtained an accuracy of 99.88% and an error rate of 0.019%.

关键词： Gold Accuracy Error analysis surveillance Feature extraction Discrete wavelet transforms Convolutional neural networks security Optimization sports

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Hybrid Autoregressive Integrated Moving Average and bi-directional Gated Recurrent Unit for Time series Forecasting

Hybrid Autoregressive Integrated Moving Average and Bi-direc...

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Data science and Network security (ICDsNs), International Conference on

作者： s. Nelson Akhil Raj Gaius Yallamelli Ahmad Alkhayyat N. Naga saranya sowmya M Department of Information Technology V.S.B. Engineering College Karur India Amazon Web Services Inc Seattle USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Forecasting in time series is given special attention in manufacturing, containing sophisticated systems in which patterns of behavior are being recorded through time series data. but it has to be mentioned that the non-strictly linear and non-stationary processes of these predictive systems make the process much more challenging in terms of accurate forecasting. A hybrid method used for searching an appropriate function by combining the outcomes from the linear model and the nonlinear model is proposed. The developed method has three phases: (i) a linear model is used for time series (ii) a nonlinear model is used for error series and (iii) hybridization of both linear and non-linear models. Autoregressive Integrated Moving Average (ARIMA) method is used in step 1 and bi- directional Gated Recurrent Unit (bi-GRU) is introduced and used in step 2. by using six real-world time series, the proposed method is evaluated using performance metrics such as Mean squared Error (MsE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error. The obtained results exposed that the proposed hybrid method outperformed than existing methods by achieving low measures with performance metrics OF 26.3 MsE, 10.3 MAE and 17.5 MAPE.

关键词： Measurement Time series analysis bidirectional control Logic gates Predictive models Network security Data science Data models Manufacturing Forecasting

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Machine Learning Algorithms for Robust software vulnerability Detection

Machine Learning Algorithms for Robust Software Vulnerabilit...

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Innovation and Novelty in engineering and Technology (INNOvA), International Conference on

作者： Gayatri bajantri Noorullah shariff C Department of Computer Science & Engineering BLDEA's V.P. Dr. P.G. Halakatti College of Engineering Vijayapur Karnataka India Department of AIML Ballari Institute of Technology and Management Bellary Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

software flaws pose a serious risk to the dependability and security of computer systems and applications in a variety of industries. because they take a long time and are prone to errors, traditional techniques of finding these vulnerabilities-like human code reviews and static analysis tools-are frequently insufficient. In order to improve the precision, effectiveness, and scalability of vulnerability detection, this paper presents a thorough technique for creating a software vulnerability detection tool that uses machine learning (ML) methods. The tool uses machine learning (ML) to automatically evaluate code samples, find possible security holes, and give developers useful information. The suggested approach consists of multiple phases, such as gathering and preprocessing data, extracting features, training and assessing models, and integrating and deploying tools. To maximise detection performance, a variety of machine learning (ML) algorithms are investigated, such as logistic regression, decision trees, random forests, support vector machines, and deep learning models. The tool's ability to adjust to new threats is guaranteed by its feedback mechanisms and ongoing monitoring. With the help of this cutting-edge technique, software engineers and security experts will be able to detect and address flaws more effectively and automatically, strengthening software systems' defences against ever-changing cyberattacks.

关键词： Training Deep learning Logistic regression Codes Machine learning algorithms Feature extraction software systems computer crime Random forests Monitoring

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Particle swarm Optimization for Efficient brain Tumor Classification Using Inceptionv3 Deep Learning Model

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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International Conference on Communication and Electronics systems (ICCEs)

作者： P. Akhila G. Prabaharan K. Pandiiyan s. L. swarna Hussain. A U. sakthivelu Department of Artificial Intelligence and Machine Learning B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science with Artificial Intelligence SRM Arts and Science College Chengalpattu India Department of Computer Science Science and Engineering Sona College of Technology Salem Tamil Nadu India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by segNet for accurate tumor segmentation. The core classification is performed using the Inceptionv3 model, further optimized through Particle swarm Optimization (PsO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors.

关键词： Deep learning Accuracy brain tumors Predictive models brain modeling Planning Reliability Particle swarm optimization Optimization Residual neural networks

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