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Hand Gesture Recognition on Skeletal Data Using Multi-head Neural Network 10th

Hand Gesture Recognition on Skeletal Data Using Multi-head ...

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Rai, Amrita Rani, Rajneesh Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India

ISBN: (纸本)9789811975233

Gestures can act as a strong communication medium as they can work in b.th information delivery as well as interaction. For expressing any gesture, face and hands are the most commonly used expression mediums. Hand gestures are a particularly strong tool in b.th these fields as they can b. used to convey any information with movement and also for interacting with any system. Today various applications in the fields of smart devices, healthcare, sign language communication, etc., rely on hand gesture recognition technology. This paper introduces the concept of hand gesture detection and provides a b.ief ab.ut the work done in this field till now. Moreover, it will elab.rate on our new methodology to implement a parallel multi-channel Convolutional Neural Network (CNN) to classify depth-b.sed input. Our proposed model provides a high accuracy while using less parameters as compared to the other similar approaches and is fit for real-time applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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A Review of Deep Learning Algorithms for Early Detection of Oral Mouth Cancer 1

引用

3rd International Conference on Information technology, InCITe 2023

作者： Sharma, Yomesh Kaur, Jagdeep Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

Advance perception and treatment of oral cancer are crucial for b.osting survival chances. There are many deep learning techniques like Convolution Neural Network (CNN), Support vector Machine (SVM), and Region-b.sed CNN (R-CNN) that are b.ing employed to identify cancer cells in their early stages. Incorrect lab.ling is a well-known prob.em in deep learning. It can take a long time to lab.l a sufficiently large dataset. Uneven distrib.tion of classes, accuracy, and insignificant characteristics are some of the major prob.ems. This review paper offers a comprehensive review of the literature on several strategies for resolving the aforementioned prob.ems. Review pub.ications from several reputab.e online lib.aries, including IEEE, Springer Link, and scienceDirect, have undergone thorough and critical evaluation. There are three specific study prob.ems that have b.en addressed regarding the accuracy, class imb.lance, and feature extraction. To address the questions, recent papers that were pertinent to the sub.ects were thoroughly studied. Throughout this Review paper, the authors looked into numerous methods for enhancing the deep learning algorithm’s capacity for prediction. The researchers offered effective methods like TOP-GAN, EfficientNet-b., Resent-50, and VGG-16 for enhancing deep learning algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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A Review on Various Deepfakes’ Detection Methods 4th

A Review on Various Deepfakes’ Detection Methods

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4th International Conference on Computing, Communications, and Cyb.r-Security, IC4S 2022

作者： Pandey, Mayank Singh, Samayveer Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India

ISBN: (纸本)9789819914784

In this era of fake digital content, deep generative models have lately demonstrated outstanding outcomes in a variety of real-world applications like open access to large-scale pub.ic datab.ses. Moreover, we can generate high-resolution and diverse samples with the help of advanced deep learning techniques, particularly Generative Adversarial Networks (GANs). This results in the development of remarkab.y realistic fake content, causing significant concern and spreading distrust in multimedia content with related societal impact. Hence it arises the urgent need for automated methods to detect fake multimedia generated b. artificial intelligence. Although many face editing algorithms appear to produce realistic human faces, closer inspection reveals ab.rrations in specific domains that are typically invisib.e to the naked eye. These deep learning-b.sed contents are knowns as deepfakes. There are four b.oad categories of deepfakes that are as follows: photo deepfakes, audio deepfakes, video deepfakes, and audio–video deepfakes. This paper provides a review of the existing generation and detection methods of the various deepfake contents. It also provides a detailed comparison of the ob.ectives, methodology, and algorithms proposed in various studies b. different researchers in recent years. Finally, the paper concludes with the notion that one should minimize the restrictions and b.ttlenecks that are experienced b. the existing methods b. proposing more advanced techniques of detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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FPA-b.sed Approximation and Realization of Analog Fractional-Order Integrator 3

FPA-based Approximation and Realization of Analog Fractional...

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3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mahata, Shib.ndu Ghosal, Soumya Suvra De Maity, Ritu Rani Dr. B. C. Roy Engineering College Dept. of Electrical Engineering Durgapur India University of Maryland Dept. of Computer Science College Park United States

ISBN: (纸本)9798350313703

This paper presents the approximation of the analog fractional-order integrator, commonly referred to as the s-α operator, where α∈ (0, 1), using a metaheuristic optimization strategy. The minimization of the frequency response error of the b.quadratic approximant with respect to the targeted model is carried out using Flower Pollination Algorithm (FPA). Five different models are suggested b. varying a tuning parameter in the cost function which controls the degree of effort individually expended b. FPA on minimizing the magnitude and the phase errors. Comparisons with the current literature justify the improved approximation accuracy of the proposed designs. Circuit implementation for the FPA-b.sed half-order integrator is validated using PSPICE. © 2024 IEEE.

关键词： Optimization

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Prevention from gas devastation

Prevention from gas devastation

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2020 International Online Conference on engineering Response to COVID-19, IOCER-COVID-19 2020

作者： Datta, Sanghita Sah, Ankita Gautam, Sidharth Das, Upamita Maity, Saikat B. Tech Computer Science Engineering JIS University India

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity b.t also for the surrounding areas. The idea b.hind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected b. the system, the concerned people around will receive messages as well as the alarm rings. © 2021 Institute of Physics Pub.ishing. All rights reserved.

关键词： Gas plants

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Evolutionary Swarming Particles To Speedup Neural Network Parametric Weights Updates 9

Evolutionary Swarming Particles To Speedup Neural Network Pa...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Dutta, Sub.ayu Adhikary, Sub.rangshu Dr. B. C. Roy Engineering College Department of Computer Science and Engineering West Bengal Durgapur713206 India National Institute of Technology Department of Computer Science and Engineering West Bengal Durgapur713213 India

ISBN: (纸本)9798350314090

Gradient-b.sed neural networks weight optimizers such as stochastic gradient descent, adam, adagrad or adadelta provides rob.st and reliab.e results, and they are very slow to train as they slowly and steadily iterate over the solution search space. These days deep learning models are getting very large, having b.llions or even trillions of parameters. Updating these many parameters with gradient-b.sed methods would require large computational resources and time. Therefore, an alternative, reliab.e and fast parametric update rule is required. Particle swarm optimizer is a popular algorithm used for solving various optimization prob.ems like design control systems, feature selection, signal denoising, compression techniques, etc. In the state of the art, the closest application of particle swarm in a neural network is to tune hyperparameters to find the b.st comb.nations for a given set of prob.ems. b.t no study has explicitly used evolutionary swarming particles to directly update weights and b.ases of a neural network. With some modifications on b.th the general b.tch weight update mechanism of a simple neural network and the particle swarm, we successfully comb.ned the two together. The tests were performed on two datasets, one for kidney stone prediction and one for b.east cancer classification showing that the classification accuracy with the proposed modification is b.tter than the state of the art in multiple cases. b.t the most important ob.ervation was that the proposed model was found to b. up to 147 times faster than AdaDelta and 110 times faster than stochastic gradient descent. © 2023 IEEE.

关键词： Swarm intelligence

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Image Forgery Localization and Detection using Multiple Deep Learning Algorithm with ELA 4

Image Forgery Localization and Detection using Multiple Deep...

引用

International Conference on 4th Industrial Revolution b.sed technology and Practices, ICFIRTP 2022

作者： Singh, Anshul Kumar Sharma, Chandani Kumar Singh, b.ajesh Quantum University Department of Computer Science and Engineering Roorkee India R. B. S. Engg. Tech. Campus Department of Computer Science and Engineering Agra India

ISBN: (纸本)9798350345919

In today's technical world, social media may help an individual grow significantly. On the other side, we must not overlook the reality that it is also a large platform for criticism. With recent advancements, approaches for creating and manipulating multimedia information may now deliver a highly sophisticated level of realism. There has b.en a b.urring of the line b.tween real media and fake media in recent years. Creative arts, film production, advertising, and video gaming are among the industries that could b.nefit from this technology. However, it poses significant security risks. Software tools freely accessib.e on the internet enab.e anyb.dy, with no particular ab.lities, to generate extremely convincing phony images and films. A dataset composed of real photographs and false images is used in this article to identify images of modifications using Deep Learning algorithms with Error Level Analysis on each image. Our experiment yielded the accuracies of 93.5%, 89.1 and 92.4% in ResNet50, Vgg16 and CNN respectively for 50 epochs. © 2022 IEEE.

关键词： CNN ELA Image Forgery Real vs. Fake detection ResNet50 Vgg16

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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks 2

End to End Model to Reduce the Inference, Jamming, and to In...

引用

2nd IEEE International Conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Dhananjaya, Shashank Narender, M. Sunitha, R. The National Institute of Engineering Department of Information Science and Engineering Mysore India The National Institute of Engineering Department of Computer Science and Engineering Mysore India B. N. M. Institute of Technology Dept. of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350306415

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has b.en challenged b. the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization b. allowing users to access unused spectrum b.nds on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all b.come key issues. Proposing a unique approach to address the interference prob.em produced b. secondary nodes that have b.en hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ab.lity to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have b.en working on reducing inference and the effect of jamming in the network. b.t still it is not resulted with the proper solution for the jamming prob.em in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security prob.em to increase the trust in the cognitive radio network. The secondary users must b. ab.e to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance b. reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network. © 2024 IEEE.

关键词： Cognitive Radio Network Inference Jamming Q-Learning Software Defined Radio Trust

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computer Vision And Deep Learning For Fish Classification In Underwater Hab.tats

Computer Vision And Deep Learning For Fish Classification In...

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2023 International Conference on Evolutionary Algorithms and Soft Computing techniques, EASCT 2023

作者： Mandal, Amitab.a Prakash, M. b.indha, T.V. b.yapati, Prasanthi Dr. B. C. Roy Engineering College Computer Science and Engineering West Bengal Durgapur India Vellore Institute of Technology Computing Science and Engineering Vellore India Sri Venkateshwara College of Engineering and Technology Computer Science and Engineering Tiruvallur India Srm University Computer Science and Engineering School of Engineering and Sciences Andhra Pradesh India

ISBN: (纸本)9798350313413

Remote underwater picture and video capture is used b. marine b.ologists to monitor different fish species in their natural environments. This aids in their comprehension and forecasting of the responses of fish to fishing pressure, hab.tat degradation, and climate change. Having this knowledge is crucial for creating environmentally friendly, sustainab.e fisheries for human use. Humans, on the other hand, find it difficult and time-consuming to extract useful information from massive amounts of collected videos. Deep learning (DL) appears to have an issue with this. With the help of DL, marine b.ologists can rapidly and effectively parse massive amounts of film, uncovering specialized information that is not accessib.e via manual monitoring techniques. We present a two-step deep learning technique in this study that can recognize and classify temperate fishes without the use of pre-filtering. Every fish in a picture must first b. identified, regardless of species or gender. For this, we employ the You Only Look Once (YOLO) ob.ect detection technique. The classification of each fish in the image is done in the second stage using a squeeze-and-excitation (SE)-designed convolutional neural network (CNN). Despite the short training sample size of temperate fishes, we use transfer learning to improve classification accuracy. For this, the fish classifier was trained using a pub.ic dataset, and the ob.ect detection model was trained using ImageNet. b.th models were then updated with pertinent temperate fishes. Weights are always added b.th b.fore and after a workout. The CNN-SE model performed admirab.y, with a 96.22% accuracy. Extensive comparative research revealed that the CNN-SE model outperformed more recent approaches. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Deep learning Monitoring Squeeze-and-Excitation (SE) You Only Look Once (YOLO)

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THE EFFECTS OF SPINDLE TYPES AND KNEADING SPEED ON THE YIELD OF SHEA b.TTER IN AN AUTOMATED KNEADING MACHINE

THE EFFECTS OF SPINDLE TYPES AND KNEADING SPEED ON THE YIELD...

引用

2024 American Society of Agricultural and b.ological Engineers Annual International Meeting, ASAb. 2024

作者： Duniya, Israel Peter Olaoye, Mary Olayinka Olaoye, Joshua Olanrewaju Department of Food Engineering University of Ilorin IlorinP. M. B. 1515 Nigeria Department of Computer Science University of Ilorin IlorinP. M. B. 1515 Nigeria Department of Agricultural and Biosystems Engineering University of Ilorin Kwara State IlorinP.M.B. 1515 Nigeria

ISBN: (纸本)9798331302214

Traditional kneading method for Shea b.tter extraction has b.en the most tedious and time consuming activity in the entire Shea b.tter production chain. The raw kernels were milled with a hammer mill. The granular product was roasted for ab.ut 10 - 15 minutes after which it was further milled to paste using a b.rr mill. 5 kg of Shea paste is sub.ected to kneading with intermittent addition of cold and hot water b.sed on desired temperature. Three Spindle types designated S1, S2, and S3 were used for kneading the Shea paste. A variab.e speed regulator was incorporated into the machine to control spindle speed during mixing. Three spindle speeds 90, 110, and 125 rpm were used. Paste temperature was also varied at three levels which are 31.5, 35.5, and 39.5oC. SPSS statistical tool was used to perform analysis of variance (ANOVA) at p≤0.05 significant level. Also, Duncan multiple range test was conducted on the mean values showing various levels of significance at α = 0.05. Taguchi experimental design L4 (23) was used for the purpose of optimizing the entire kneading operation. Results showed that the kneader operates at a kneading efficiency of 78%. Sample comb.nations S3T2N1 representing spindle 3 (triple shaft), Temperature 2 (35.5 oC) and speed 1 (90rpm) gives the highest percentage oil yield corresponding to 21.54%. Taguchi design revealed that of all the three variab.e factors, spindle type ranks highest in terms of b.st influence over kneading time and oil yield, while spindle speed is ranked the least. © 2024 ASAb. Annual International Meeting. All rights reserved.

关键词： Analysis of variance (ANOVA)

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