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2nd International Conference on Cybersecurity and Evolutionary Data engineering, ICCEDE 2022

作者： Dutta, Anurag Munjal, DeepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Computational complexity Dnf sort Partitioning Sorting algorithms

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Lips and Tongue Cancer Classification Using Deep Learning Neural Network 6

Lips and Tongue Cancer Classification Using Deep Learning Ne...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management and Research Gwalior India Mits Department of Computer Engineering Gwalior India Sos in Computer Science and Applications Jiwaji University Gwalior India

ISBN: (纸本)9798350346961

One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.. © 2023 IEEE.

关键词： Neural network models

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A Genetic-Muted Leader Scheme with CNN-Based IDS for Industrial Internet of Things Networks 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Reddy, Boyella Mala Konda Abdul Azeez Khan, A. Sathick, K. Javubar Raj, L. Arun Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

The industrial industry has adopted web and cloud-based technologies widely, which has accelerated the IoT ecosystem’s expansion. Industrial Internet of Things (IIoT) has established a large network due to the sheer volume of information and linked equipment. IIoT networks are made to be vulnerable in terms of cybersecurity. So as to ensure the security of the IIoT networks, intrusion detection systems (IDS) must be developed. This study suggests an IDS-based on a Genetic-Muted Leader Scheme (GMLS)-CNN for usage in IIoT networks. The data collection and preprocessing steps improve the detection accuracy. The next step is to classify data using convolutional neural networks (CNNs), with GMLS used to fine-tune the parameters for the most accurate results. The two datasets such as UNSW-NB15 and X-IIoTID and named as TD were used to distinguish normal and problematic data, which allowed the study to evaluate several categorization techniques. Among the other schemes, the GMLS-CNN model had the maximum accuracy rate for IDSis 98.7% (X-IIoTID) and 95.21% (UNSW-NB15). Additionally, the model’s success in correctly detecting attacks based on the various types of attacks reported in the datasets was assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. Advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

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Analysis of Various Image Segmentation Techniques on Retinal OCT Images 3

Analysis of Various Image Segmentation Techniques on Retinal...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sheeba, T.M. Albert Antony Raj, S. Anand, M. Srm Institute of Science and Technology College of Science and Humanities Department of Computer Applications Chennai India Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9781665462167

Image segmentation is one of the most important processes involved in image processing. Segmenting an image is breaking it up into smaller pieces, or segments. These are the areas where it is most helpful because processing the complete image would be inefficient for tasks like object recognition or image compression. Image segmentation is the process of dividing an image's elements for further processing. This article looked at a number of image segmentation techniques, including the threshold approach, edge-based method, and clustering-based method. The segmentation method that is most effective for separating images in OCT images is clustering-based segmentation. To reduce the speckle noise during preprocessing, the wiener filter approach is used. With regards to the threshold, edge-based (Sobel, Canny, Robert's), and clustering-based segmentation techniques, calculate MSE (mean square error) and PSNR (peak signal to noise ratio) values for the segmented image quality. © 2023 IEEE.

关键词： Image segmentation

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A Robust MAES Encryption And Decryption Algorithm using Ankaru Model Through Fog Computing

A Robust MAES Encryption And Decryption Algorithm using Anka...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Karunya, S. Sara, Belina V.J. Angayarkkani, K. Sowmiya, J. Sonia, A. Jeganraj, P. Dwaraka Doss Goverdhan Doss Vaishnav College Department of Information Technology & Bca Chennai India Srm Institute of Science & Technology Department of Computer Applications Chennai India Aalim Muhammed Salegh College of Engineering Department of Cse Chennai India Srm Institute of Science & Technology Master of Computer Applications Chennai India Srm Institute of Science & Technology Department of BCA-DS Chennai India

ISBN: (纸本)9798350360660

The world as a whole now depends on the data that people have stored online and how these developments are tracked in the future. To satisfy the requirements of safeguarding the information of an individual, a company, or a rapidly growing sector, there is only one standard. The first phase, encryption, and the second, decryption, are absolutely necessary to complete. Data is encrypted for protection, then it is decrypted for retrieval. The Advanced Encryption Standard provides the highest level of security for data protection, ensuring that its processing remains hidden. Data on edge devices is encrypted using the AES method by Fog. Since AES has never experienced a data breach, safety and security are the most fundamental truths that may be discovered through its use. Data from Edge devices is encrypted and then modified using fog computing to secure it using the AES technique. The data is then recovered by decryption. The secret key is primarily concerned with data entering and returning to the source, which transforms the proper propagation outcome. © 2024 IEEE.

关键词： advanced encryption standard advanced encryption standard and transformation cryptography decryption modified

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IoT-Based Crop Yield Prediction System in Indian Sub-continent Using Machine Learning Techniques

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Remote Sensing in Earth Systems sciences 2023年第3-4期6卷 156-166页

作者： Nithya, V. Josephine, M.S. Jeyabalaraja, V. Department of Computer Applications Dr. M.G.R. Educational and Research Institute Tamil Nadu Chennai India Department of Computer Science Engineering Velammal Engineering College Tamil Nadu Chennai India

The agricultural sector holds significant importance in the socio-economic structure of India. The inability of farmers to make informed decisions regarding the optimal crop selection for their land, based on non-scientific and traditional methods, poses a significant challenge for a nation where farming constitutes the livelihood of nearly 58% of the population. At times, farmers have experienced challenges in selecting appropriate crops that align with the soil conditions, sowing season, and geographical location. By collecting data from sensors in the fields, ranchers can easily ascertain the extent of the crops, which helps preserve scarce resources and lessens the negative effects of weeds on the environment. The majority of Indian farmers choose conventional farming methods when deciding how their crops should be grown. Regardless, ranchers do not jeopardize agricultural yields, which are sensitive to abiotic factors such as soil type. In this proposed method, two types of datasets have been collected from the southern and northern parts of the country through IoT-based sensors. The crop recommendation system has been processed with various machine learning algorithms like logistic regression, KNN, decision tree, and support vector machine through the collected dataset. The accuracy of 97% and 96% has been achieved for processed datasets in the southern and northern parts of India. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Accuracy Crop recommendation Decision tree IoT KNN Logistic regression Machine learning Precision farming SVM Training

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saha, Piyush Ghosh, Sumon Bhandari, Dinabandhu Calcutta Institute of Engineering and Management Dept. of Electrical Engineering Kolkata India Heritage Institute of Technology Dept. of Computer Applications Kolkata India Heritage Institute of Technology Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350314144

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field. © 2023 IEEE.

关键词： Generative adversarial networks

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Face Recognition using Adaboost Classifier For The Visually Impaired 8

Face Recognition using Adaboost Classifier For The Visually ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Avinash, N.J. Krishnaraj Rao, N.S. Rama Moorthy, H. Mangalore Institute of Technology and Engineering Department of Electronics and Communication Engineering Mangalore Karnataka Moodabidre India Nmam Institute of Technology Department of Information Science and Engineering Karnataka Nitte India Nitte Institute of Professional Education Department of Computer Applications Karnataka Mangaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The paper suggests an approach to facial recognition of physical challenges. The most difficult challenge for blind persons is identifying impediments outside and behind them. They mostly rely on other people to help them make up for their blindness. To overcome this, they need to be supported by someone else people. In their daily lives, they face many problems when it comes to moving from one place to another. There is no guidance to help them when they need it. This study suggests a facial recognition system for those with vision impairments utilizing the Raspberry Pi to help prevent these situations. There is a camera designed for face recognition and a microphone designed for voice recognition in this case, so these components can be combined and people with visual disabilities can use it easily and behaves reasonably. In this work, a method is presented for detecting, classifying, and locating objects within complex environments. The paper applies the techniques of image processing and Adaboot classifiers to achieve an accuracy of more than 90 © 2024 IEEE.

关键词： Face recognition

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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