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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Singh, Anuj Anand, Tanmay Sharma, Sachin Singh, Pankaj Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665435871

Due to the increase in crime and terrorism in most parts of the world, security surveillance is becoming increasingly important. A computer vision-based system for detecting weapons for real-time security surveillance is designed in this work. For identification, detection, and notifying the appropriate authorities, the system employs the YOLO V4 (You Only Look Once) algorithm. This neural network can be trained using images, videos, and live streaming videos. This model incorporates Internet-of-Things (IoT) smart devices that are interconnected and automated in weapon detection. This model's accuracy varies depending on the quality of the images and videos used in the detection process. Here, the proposed research work has discovered that the detection process is affected by the type of hardware that has been utilized to run the algorithm, ranging from low-quality image/video detection with 70% accuracy to high-quality image/video detection with 95% accuracy. © 2021 IEEE.

关键词： computer vision

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A Robust Ensemble Learning Approach for Medical Image Segmentation with Transfer Learning

A Robust Ensemble Learning Approach for Medical Image Segmen...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritu Shree Kavitha R Shweta Singh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

This paper proposes a robust ensemble mastering approach for clinical picture segmentation. The proposed technique combines a convolution neural community (CNN) with a switch studying-based totally ensemble model. The CNN is pre-educated with a dataset containing clinical photo modalities, particularly Magnetic Resonance Imaging (MRI) and computed tomography (CT)., a weight-averaged ensemble model is acquired between the two scientific photograph modalities. This version is an initial way to the clinical image segmentation problem. Eventually, the ensemble model is great-tuned with additional imaging statistics from an unmarried modality to improve the segmentation accuracy. The proposed technique is evaluated on a range of datasets, and the effects display that it achieves competitive performance compared to the country of the artwork. Moreover, the experiments reveal that combining the transfer gaining knowledge of-based totally ensemble mastering approach with extra imaging information enhances the accuracy of medical picture segmentation.

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BREAKING SPEAKER RECOGNITION WITH PADDINGBACK

arXiv

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arXiv 2023年

作者： Ye, Zhe Yan, Diqun Dong, Li Shen, Kailai Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China

Machine Learning as a Service (MLaaS) has gained popularity due to advancements in Deep Neural Networks (DNNs). However, untrusted third-party platforms have raised concerns about AI security, particularly in backdoor attacks. Recent research has shown that speech backdoors can utilize transformations as triggers, similar to image backdoors. However, human ears can easily be aware of these transformations, leading to suspicion. In this paper, we propose PaddingBack, an inaudible backdoor attack that utilizes malicious operations to generate poisoned samples, rendering them indistinguishable from clean ones. Instead of using external perturbations as triggers, we exploit the widely-used speech signal operation, padding, to break speaker recognition systems. Experimental results demonstrate the effectiveness of our method, achieving a significant attack success rate while retaining benign accuracy. Furthermore, PaddingBack demonstrates the ability to resist defense methods and maintain its stealthiness against human perception. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Improving Efficiency and Accuracy in Clinical Diagnostic Decision Making with Deep Learning

Improving Efficiency and Accuracy in Clinical Diagnostic Dec...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Vikas Kumar Kharbas N Gobi Dinesh Kumar Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Deep learning is a modern form of synthetic intelligence (AI) based on large quantities of statistics and algorithms that allow computer systems to approximate features and solutions, ensuing in more excellent, efficient, and accurate selections. In the scientific field, deep gaining knowledge is increasingly used to help clinicians with diagnostic decision-making, decreasing the value and attempt associated with diagnostic approaches. This paper discusses the potential of deep learning to enhance the performance and accuracy of medical diagnostic selection-making. The paper provides an overview of the cutting-edge nation of the field, with a particular awareness of picture processing and its application to improve accuracy and decrease the price associated with scientific diagnoses. It also provides a few capacity challenges and pitfalls and outlines the issues essential to ensure a hit implementation and successful results. Eventually, it offers a few recommendations for similar studies to increase deep getting to know algorithms further. Deep gaining knowledge has been broadly explored in recent years as a powerful tool for enhancing the accuracy and efficiency of clinical diagnostic decision-making. Deep studying models can investigate patterns in medical datasets, deliberating the traits of affected person information and ailment signs to generate extra specific and reliable diagnoses. By incorporating extra facts such as transcripts from affected person-clinician interactions and medical imaging, those fashions may be trained to assist clinicians in identifying illnesses early and provide well-timed and correct analysis. Moreover, deep learning allows for incorporating case-primarily based reasoning and presents the capacity to combine scientific knowledge in real-time. By having extra sturdy and accurate models, clinicians can reduce the time required to diagnose a condition and make treatment choices. Deep gaining knowledge also lets in for the integration o

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Investigating the Integrity of Irrigation Systems Through Hyper Spectral Image Analysis

Investigating the Integrity of Irrigation Systems Through Hy...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Zahid Ahmed Preethi D Dhananjay Kumar Yadav Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

This research aims to use hyperspectral picture analysis to research the integrity of irrigation systems, hoping to reduce power and water utilization. Traditional methods for measuring irrigation integrity are timeingesting, luxurious, and unreliable. With the software of the advanced imaging era, this project seeks to create a detailed model of an irrigation system so one can discover and diagnose troubles quickly and appropriately. A huge-statistics technique utilizing spectral statistics mixed with professional gadget wisdom gives the potential irrigation structures control. analysis might be conducted using a combination of airborne imaging and computational strategies along with satellite tv for pc and in-area evaluation. Those techniques will assist in offering a value-effective monitoring machine for identifying and diagnosing irrigation troubles and detecting modifications that can suggest water misuse or inefficient layout. Using this era, we intend to enhance irrigation structures' overall performance and integrity.

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Designing of Financial Time Series Forecasting Model Using Stochastic Algorithm Based Extreme Learning Machine 2nd

Designing of Financial Time Series Forecasting Model Using S...

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2nd International Conference on Intelligent and Cloud Computing, ICICC 2021

作者： Hota, Sarbeswara Mohanty, Arup Kumar Mishra, Debahuti Satapathy, Pranati Jena, Biswaranjan Department of Computer Application Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Information Technology Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Applications Utkal University Odisha Bhubaneswar India Tata Consultancy Services Odisha Bhubaneswar India

ISBN: (纸本)9789811698729

The financial forecasting research domain includes many directions, out of which forecasting of currency exchange rate attracts many researchers. The researchers have used various neural networks for the development of forecasting models due to the nonlinear and chaotic nature of exchange rate data. Different stochastic optimization algorithms have been hybridized with neural networks for further optimization of the network parameters. In this paper, Extreme Learning Machine (ELM) technique is applied to predict the exchange rate and the recently developed Sine Cosine Algorithm (SCA) is hybridized with this technique. The proposed ELM-SCA model is studied with respect to its prediction performance on a sample dataset for one-day ahead and seven-day ahead prediction. The experimental results demonstrate that the suggested ELM-SCA model produces better results than the ELM model in forecasting of SGD to INR. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Emotion State Detection via Speech using Deep Learning

Emotion State Detection via Speech using Deep Learning

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Rashmi Gupta Jeetendra Kumar Suvarna Sharma Department of Computer Science and Application Atal Bihari Vajpayee University Bilaspur Chhattisgarh India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Oral communication is the most efficient and common way to interact with someone. Due to this, voice signalling has attracted researchers for its potential in computer-human communication. Identifying emotions through speech is complex due to various factors like feature selection and vocal diversity. This study uses the RAVDESS and SAVEE datasets and employs techniques including Multilayer Perceptron, Random Forest, Support Vector Machine, k-Nearest Neighbours, Logistic Regression, Gaussian-Neural Network, and Convolutional Neural Network. The identified features encompass the MelFrequency Cepstral Coefficients (MFCC), Chroma, and Mel spectrogram. To conduct a comparison of accuracy rates, we employed seven classifiers across two datasets. Consequently, CNN model demonstrated superior performance in both the RAVDESS and SAVEE databases, with accuracy rates of $\mathbf{9 6. 7 5 \%}$ and $\mathbf{9 2. 3 6 \%}$ respectively, surpassing other classifiers.

关键词： Support vector machines Logistic regression Accuracy Neural networks Oral communication Multilayer perceptrons Convolutional neural networks Mel frequency cepstral coefficient Spectrogram Random forests

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Maximizing the Accuracy of Time Series Analysis for the Early Detection of Rhabdomyosarcomas

Maximizing the Accuracy of Time Series Analysis for the Earl...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritu Shree Prabhu A Anupam Kumar Gautam Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Rhabdomyosarcomas are a rare malignant tender tissue tumor that generally gives in younger children and teenagers. Early prognosis and remedy are essential for successful outcomes. Time collection evaluation is a valuable tool for recognizing styles and trends in medical facts, mainly for rare situations, which include Rhabdomyosarcomas. It has consequently been increasingly employed to detect early signs and symptoms of the ailment. On this look, we are conscious of investigating and optimizing techniques for time collection analysis. It is an excellent way to enhance its application and accuracy in identifying early symptoms and signs of rhabdomyosarcoma. We examine present strategies and suggest improvement techniques, along with function extraction and system mastering techniques. We further inspect the effectiveness of our strategies by conducting experiments on a dataset installed from scientific facts and literature of rhabdomyosarcoma instances. Those experiments show promising effects, indicating that our proposed strategies can considerably increase the accuracy and sensitivity of time series evaluation for the early detection of rhabdomyosarcoma and cause higher prognoses for affected sufferers. The focal point of this study is to maximize the accuracy of time series analysis for the early detection of rhabdomyosarcoma. Time collection analysis includes: • The gathering of temporal information from multiple sources. • The assessment of these records. • The interpretation of correlations between the facts points. This study aims to utilize these techniques to discover diffused adjustments in affected person information so that you can perceive the onset of the disorder in advance than would be possible with traditional techniques. The examination will expand algorithms to systematically and accurately procedure the temporal statistics and discover adjustments indicative of Rhabdomyosarcomas. In addition, the look will rent gadget learning to boost the dete

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Hierarchical Representation Learning for Improved Diagnosis of Prostate Cancer on MRI Scans

Hierarchical Representation Learning for Improved Diagnosis ...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Prabhu A Megha Pandeya Harshita Kaushik Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

It focuses on using hierarchical illustration mastering (HRL) for the progressed prognosis of most prostate cancers on MRI scans. HRL is a gadget getting-to-know technique using a hierarchy of function vectors to encode record sets, allowing extra complicated non-linear styles to be recognized and applied. In this examination, HRL is compared to a fashionable convolution neural network (CNN) classifier for the challenge of prostate cancer analysis on MRI scans. The effects show that HRL outperforms the CNN classifier, providing a higher accuracy fee and universal predictive performance. Furthermore, HRL results in fewer fake positives and extra correct type accuracy. Its improved overall performance suggests that utilizing HRL in medical decision-making can offer a more correct, practical analysis of most prostate cancers.

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Developing Multi-Scale Patch Representations for Low Energy Data Aggregation Using Unit Block Building Model

Developing Multi-Scale Patch Representations for Low Energy ...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Bhuvana J Ritesh Kumar Vikas Kumar Kharbas Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

Developing Multi-Scale Patch Representations using Low energy information Aggregation is a paper examining the development of multi-scale representations from massive aggregated datasets for pc vision duties. The paper presents the low-strength records aggregation (LEDA) framework, an aggregate of electricity-green processes to extract multi-scale representations from big datasets. This paper provides a unique method for developing multi-scale patch representations for image analysis and using low-energy data aggregation. The technique utilizes records aggregation to create texture capabilities from which neighbourhood patch representations are constructed. These representations are then used to assemble more than one scale of features for analysis in pc vision structures. The proposed approach is evaluated for texture segmentation, and the consequences endorse that the patch representations received from the proposed approach outperform those acquired from conventional strategies. The paper additionally discusses the consequences of using different levels of information aggregation for generating patch representations. Subsequently, the paper explores the want for further studies to assess the overall performance gains acquired from the proposed method.

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