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

作者： Preet Chandan Kaur Leena Ragha Department of Computer Engineering Ramrao Adik Institute of Technology Dr. D.Y. Patil Deemed to be University Nerul Navi Mumbai India Department of Computer Science and Engineering BLDEA's V.P. Dr. P. G. Halakatti College of Engineering and Technology Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Feature extraction informs fundamental computer vision and pattern recognition research. Examines how to obtain useful features from raw datasets, such as images or video frames. This stage is crucial in identifying images, recognizing objects, and analyzing frames. Effortless and accurate feature extraction may enhance machine learning models when there is a lot of visual input. video lectures with slides, text, and diagrams can benefit from feature extraction. A study will compare video lecture analysis feature extraction algorithms and identify research gaps. We will use our survey methods to compare the performance of seven representative feature extraction algorithms: shape Local binary Texture (sLbT), Local Optimal Oriented Pattern (LOOP), Local vector Pattern (LvP), scale-Invariant Feature Transform (sIFT), speeded-Up Robust Features (sURF), Gabor Filters, and Local binary Patterns (LbP). sLbT and LOOP have the highest accuracy (90%), while LbP holds the lowest (85%). sIFT and sURF are widely scalable but slower to compute. Gabor and LbP filters are quick, but inaccurate and scalable. The survey found that sLbT, LOOP, and LvP are better feature extraction methods for video educational processing than others.

关键词： surveys visualization Technological innovation Accuracy shape Transforms streaming media Feature extraction vectors Gabor filters

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RETRACTED ARTICLE: The importance of light-weight encryption cipher in restricted IoT systems to make intelligent technology safer for devices

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Applied Nanoscience (switzerland) 2023年第2期13卷 989-989页

作者： Kumar, K. Kranthi srikanth, b. Kasiviswanadham, Y. subbarao, Ch. D. v. Indira, D.N.v.s.L.s. Pratap, N.L. IT Department Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India CSE Department SRK Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering S.V.U. College of Engineering Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

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Document Image Retrieval: Issues and Future Directions

Document Image Retrieval: Issues and Future Directions

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2021 International Conference on Computational Intelligence and Computing Applications, ICCICA 2021

作者： Dixit, Umesh D. shirdhonkar, M.s. B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Electronics and Communication Engineering Vijayapura586103 India B.L.D.E.A's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Vijayapura586103 India

ISBN: (纸本)9781665420402

Document image retrieval is an interesting and popular area of research that has been evolved into many stages. The current trends show that it is still an emerging area due to the availability of document scanner app with mobiles and handheld devices. The objective of this paper is to provide an insight into document retrieval, classification, a general architecture, related issues and new opportunities for research in the domain. © 2021 IEEE.

关键词： Image retrieval

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Cancer Cell Detection and Classification from Digital Whole slide Image 5th

Cancer Cell Detection and Classification from Digital Whole ...

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5th International Conference on smart Technologies in Data science and Communication, sMART-DsC 2022

作者： Gavade, Anil b. Nerli, Rajendra b. Ghagane, shridhar Gavade, Priyanka A. bhagavatula, venkata siva Prasad Department of E&C KLS Gogte Institute of Technology Karnataka Belagavi India Karnataka Belagavi India Department of Biotechnology KAHER’s Dr. Prabhakar Kore Basic Science Research Center V. K. Institute of Dental Sciences Campus Karnataka Belagavi India Department of Computer Science and Engineering KLE Tech University Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belagavi India Hyderabad Medtronic India

ISBN: (纸本)9789811968792

The World Health Organisation has identified cancer as one of the foremost causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatment option for the patient. Pathological data have huge tumour information that can be used to diagnose cancer. Digitizing pathological data into images and its analysis using Deep learning applications will be a significant contribution to clinical testing. Due to advancements in technology, artificial intelligence (AI) and digital pathology can now be combined allowing for image-based diagnosis. This study uses residual networks (ResNet-50) and convolutional neural network (CNN), which is pre-trained on ImageNet dataset to train and categories lung histopathology images into non-cancerous, lung adenocarcinoma, and lung squamous cell carcinoma delivering an accuracy of 98.9%. Experimentation results show that the ResNet-50 model delivers finer classification results when compared to state-of-the-art methods. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Artificial intelligence Classification Convolutional neural network Deep learning Graphic processing unit Histopathological image Residual networks

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Design of adaptive hybrid classification model using genetic-based linear adaptive skipping training (GLAsT) algorithm for health-care dataset

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Advances in Computational Intelligence 2022年第2期2卷 1-10页

作者： Ramasamy, Manjula Devi Periasamy, Keerthika Periasamy, suresh Muthusamy, suresh Panchal, Hitesh solanki, Pratik Arvindbhai Panchal, Kirti Department of Computer Science and Engineering Kongu Engineering College (Autonomous) Erode India Department of Information Technology Kongu Engineering College (Autonomous) Erode India Department of Electronics and Communication Engineering Kongu Engineering College (Autonomous) Erode India Department of Mechanical Engineering Government Engineering College Patan India B.S. Patel Polytechnic Ganapt University Mehsana India B & B Polytechnic Vallabh Vidyanagar Anand India

Machine-learning techniques are being used in the health-care industry to improve care delivery at a lower cost and in less time. Artificial Neural Network (ANN) is well-known machine-learning techniques for its diagnostic applications, but it is also increasingly being utilized to guide health-care management decisions. At the same time, in the healthcare industry, ANN has made significant progress in solving a variety of real-world classification problems that range from linear to non-linear and also from simple to complex. In this research work, an Adaptive Hybrid Classification Model named as Genetic-based Linear Adaptive skipping Training (GLAsT) Algorithm has been proposed for the health-care dataset. It has been designed as two-stage process. In first stage, Genetic Algorithm (GA) is adapted to optimize the Learning rate. After optimizing the Learning rate, the optimal Learning rate has been set to the ANN model is ŋ = 1e−4. In the second stage, The training process is carried out using the Linear Adaptive skipping Training (LAsT) algorithm, which reduces the total training time and thus increases the training speed. As a result, the highlighted characteristics of LAsT have been integrated with GA to accomplish rapid classification and enhance computational efficiency. On 8 different health-care datasets extracted from the UCI Repository, the proposed GLAsT algorithm outperforms both the bPN and LAsT algorithms in terms of accuracy and training time, according to simulation results. The result analyses have proved that the efficiency of this proposed GLAsT Algorithm outperforms over the existing techniques such as bPN and LAsT in terms of accuracy and training time. On various datasets, experimental results show that GLAsT improves accuracy from 4 to 17% over bPN training algorithm and reduces overall training time from 10 to 57% over bPN training algorithm.

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A Real-Time spam Identification scheme Over social Networking Environment Using Deep Learning Principles

A Real-Time Spam Identification Scheme Over Social Networkin...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mahalakshmi P v. Mahalakshmi E.s. vinothkumar senthilkumar b M Dinesh R. Krishnaprasanna Department of IT Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering(IOT) Saveetha Engineering College Chennai Tamil Nadu India Department of EEE V.S.B. Engineering College Karur Tamil Nadu India Department of VLSI Microelectronics Saveetha Engineering College SIMATS Chennai Tamil Nadu India Department of ECE Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

Every single day, millions of people all around the world utilize various social networking platforms. The usage of social media platforms like Twitter and Face book may result in both positive and bad results for users. These outcomes are not mutually exclusive. The most popular social networking sites have recently been a primary target for spammers who wish to disseminate large numbers of undesired and maybe harmful information. For example, Twitter has quickly become one of the most commonly used platforms ever, which has led to a large increase in the amount of spam that is posted on the network. Users who really use Twitter are irritated and frustrated by tweets from bogus accounts that promote meaningless products, services, or websites. There has also been an increase in the ease with which hazardous chemicals may be transmitted by presenting users with misleading information while using bogus identities. This has led to an increase in the likelihood that people would be harmed. In the field of contemporary social media, two of the most popular research subjects are the elimination of spam and the verification of users on Twitter. In the event that they continue to disseminate dangerous advertisements, spam accounts on social networking websites pose a substantial threat to the safety of the internet and should be eliminated as quickly as possible. This article explores the origins of spam accounts on social networks like Twitter, as well as the distinguishing characteristics of spam accounts, with the goal of improving spam identification. Twitter is one of the most popular online social networks (OsNs), and its members include ministers, business moguls, Hollywood actors, and Fortune 500 companies. The platform's 313 million monthly active users are responsible for publishing around 500 million tweets each and every month on it. Due to Twitter's rising popularity, spammers have developed an interest in the platform. These malicious actors exploit the servi

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Human Activity Recognition Using LRCN

Human Activity Recognition Using LRCN

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North Karnataka subsection Flagship International Conference (NKCon), IEEE

作者： santosh Kumar s Dewar Pallavi Anand Hiremath sahebgouda Patil Pavan Mahendrakar Department of computer science and Engineering BLDEA's V.P.Dr.P.G.Halakatti College of Engineering and Technology Vijayapur

Using the capabilities LRCN it obtained to increase the multimedia, recognize human movement in videos. HAR is a one ongoing research field. On various benchmark datasets, new records have been established using video as a series of frames. Convolutional neural networks (CNN) and long short-term memories were proposed in this (LsTM). CNN network is applied to get feature vectors for each videos and LsTM network is use to classify the videos. standard activities contains many activities horse racing, taichi, walking with dog, basketball, swing, etc. In this proposed system we have achieved 99.5% of Accuracy.

关键词： Codes Recurrent neural networks Portable computers Machine learning streaming media software Internet

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Theoretical bound and Compensation for Residual Error Probability of GRAND-CRC-based Functional safety Communication

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IEEE Transactions on Communications 2025年

作者： Zhan, Ming Pang, Zhibo Wu, Fang Zhou, Qiang Zhang, shiqing Zhang, Jianwu Yu, Kan Taizhou University College of Electronic and Information Engineering Zhejiang Taizhou318000 China ABB Corporate Research Forskargränd 7 VästeråsSE-721 78 Sweden School of Electrical Engineering and Computer Science Department of Intelligent Systems StockholmSE-114 28 Sweden Southwest University College of Economics and Management Chongqing400715 China Hangzhou Dianzi University College of Telecommunication Engineering Hangzhou310018 China La Trobe University Bendigo Campus School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology Edwards Rd Flora HillVIC3552 Australia

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (sPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded sPDU can minimize sPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to sPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an sPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102 times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications. © 1972-2012 IEEE.

关键词： Decoding

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Autonomous Mobility: A smart Wheelchair Ecosystem by Iaas Cloud Model using AWs

Autonomous Mobility: A Smart Wheelchair Ecosystem by IaaS Cl...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： s.K. saravanan More Praveen s. Aghalya b. shadaksharappa N. Mohankumar v. Nandagopal Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Sreenidhi Institute of Science and Technology Hyderabad Telangana India St. Joseph’s College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India B.V. Raju Institute of Technology Telangana India Mohan Babu University Tirupathi Andrapradesh India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

A cutting-edge smart wheelchair ecosystem is proposed in this paper which is driven by Infrastructure as a service (Iaas), with the primary emphasis being placed on Amazon Web services (AWs). Through the use of Raspberry Pi, other advanced sensors, and AI algorithms, the system is able to accomplish extraordinary levels of autonomous movement, obstacle avoidance, and individualized user support. Iaas design and AWs makes dynamic scalability, fast data processing and seamless integration of cloud-based services much easier to achieve. The use of virtual computers, cloud storage, and the AWs Internet of Things service provide a solid basis for real-time communication and decision-making. AWs offers a variety of security measures and monitoring tools to guarantee the dependability of the system and the integrity of user data. This sophisticated combination of hardware and AWs Infrastructure as a service not only establishes a new benchmark for intelligent wheelchair technology but also highlights the potential for adaptive, cloud-enabled assistive devices to greatly improve persons with restricted mobility and their quality of life.

关键词： Cloud computing Technological innovation Web services Wheelchairs scalability Ecosystems solids

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Hierarchical Model for Email Fraud Detection Using Naïve bayes and svM

Hierarchical Model for Email Fraud Detection Using Naïve Ba...

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Women in Computing (InCoWoCo), International Conference for

作者： I. vasudevan M.Mohamed Nasurudeen M.senthil Kumar R.M.Dilip Charaan s.Ashok Kumar Leena Jenefa CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India CSE V.S.B.Engineering College Karur India AIDS Erode Sengunthar Engineering College Erode India MBA Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Email fraud detection is one of the major problems in our society with the quick rise of internet frauds and spam nowadays. some people were using it for illegal and evil purposes like phishing and fraud. The spammers are pretending like a genuine person in the spam emails for creating a fake profile and fake account. In this paper, we propose a method for email fraud detection that combines the strengths of the Naive bayes algorithm and support vector Machine (svM) in machine learning. The Naive bayes algorithm is one of the most popular and effective in handling large datasets, and this can be used to model their relationships between words in emails and their properties of being spam or ham. Meanwhile, svM is able to separate classes in high-dimensional spaces, and it is also used for the accuracy classification. by integrating these two approaches, the proposed method aims to improve the detection of spam emails by using the advantages of both algorithms. This mixed-breed model offers a strong solution to the challenge of distinguishing between spam and not spam emails. Most of the existing methods have less accuracy in detecting spam. The proposed Naïve bayes and svM algorithms achieve higher accuracy of 97.49% for email fraud detection and fast processing speed compared to other proposed methods.

关键词： support vector machines Accuracy Machine learning algorithms Filtering Unsolicited e-mail Phishing vectors Fraud Naive bayes methods Classification algorithms

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