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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tripathi, Achyut Mani Mishra, Aakansha Indian Institute of Technology Guwahati Department of Computer Science & Engineering Assam India

ISBN: (纸本)9781728163277

Knowledge distillation(KD) technique is widely known for its outstanding ability to train a compact student model under supervision of a cumbersome pre-trained teacher network. The traditional KD technique focuses only on distilling dark knowledge using logits of a teacher network while neglecting the information regarding contrastive representation. To this end, we propose a new KD loss function that enables a student network to learn informative contrastive distribution and fine grained information from spectrogram representation of a signal thus enhancing performance of a student network for sound classification task. The experiments are conducted on two benchmark sound classification datasets, viz. ESC-10 and Audio MNIST, which illustrates that the student network trained using the proposed KD loss function outperformed the competitive KD techniques. © 2023 IEEE.

关键词： Contrastive learning deep network ensemble knowledge distillation sound classification

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Web Based Attendance Management System Using Geo-Location 8

Web Based Attendance Management System Using Geo-Location

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8th IEEE International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Vishanthanshree, R. Sai Sucharith M, Venkata Naga Vijiamutha Mary, A. Mercy Paul Selvan, M. Jancy, S. Sathyabama Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350347791

In Attendance Management System by using Geo-Location, the aim is to take attendance by using Geo-Location to track the information about the user or particular user whether the user is present or not in that particular location which was given by admin and this gives the total information about the user. the recent trend in the attendance management system by using finger print and the face detection but in some this will not work and it is time taking process, by using Geo-Location we can mark the attendance it makes easier for authentication. The paper focuses on using of the Geo-Location for the attendance system. © 2023 IEEE.

关键词： Face recognition

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Tomato Crop Diseases Detection and Classification using ResNet50, DenseNet and Bins Approach 4

Tomato Crop Diseases Detection and Classification using ResN...

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4th International Conference for Emerging technology, INCET 2023

作者： Koli, Basil D'Silva, Orvil Butelho, Jaden Aloz, Admon Lobo, Nigel Sonawane, Kavita St. Francis Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350335750

Detection and Classification of various crop diseases can help to prevent a disastrous situation. If left undetected, many diseases damage the plant, thus reducing the crop yield produced by such plants. Therefore, in this paper, two diseases have been worked on. The two tomato plant diseases that have been focused on are Tomato leaf mold and Tomato target spot. Past practices to detect these diseases have proven to be time-consuming and inefficient due to poor accuracy. The primary focus of this work is to mainly detect these diseases accurately and quickly, because such diseases need to be detected in the premature stage to avoid any major harm to the plant's yield. Proposed system is aimed at addressing the existing challenges. It accepts the image as an input that will be preprocessed and will undergo the detection and classification stages. Feature extraction plays a significant role in such systems. This research contribution mainly deals with training the system using deep learning (DL) models and spatial domain image processing technique namely Convolution Neural Networks (CNN) with ResNet50, DenseNet and histogram partitioning based Bins approach. The novelty added by applying Bins approach for feature extraction clearly defines the image contents being focused on as features which aids in choosing the significant features and ignoring the unwanted features that helps in reducing the feature dimensionality. Further it is followed by application of various classifiers such as Logistic Regression, Linear SVM and Decision Trees over bins feature space. All the proposed algorithms are applied on a common dataset called PlantVillage and their performances are evaluated, analyzed and compared using the same set of parameters like precision, recall, accuracy and F1-score. Results obtained reflect that all the approaches achieved accuracy greater than 90%. Proposed novelty by Bins approach proved equally better with Logistic Regression for all the parameters. © 202

关键词： Decision trees

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Electrocardiogram Based Authentication Scheme for Smart Home Security

Electrocardiogram Based Authentication Scheme for Smart Home...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Ocheni, Jeremiah Immanuel Johnraja, J. Leelipushpam, Getzi Jeba Kathrine, G. Jaspher W Silas, Salaja Stewart Kirubakaran, S. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333244

Authentication plays a major role in the security of smart home devices. This has increased a lot of interest in the research community leading to numerous authentication scheme that have been proposed for the smart home environment to be secured for the users. In this paper, Electrocardiogram (ECG) based authentication which assists in enhancing the security of the smart home environment is deliberated. ECG had extreme specific individual characteristic qualities, and research has shown that these traits hold true during a person's lifespan, assuming no biological alterations have taken place. The computing model uses the ECG technique to generate a template from the collected ECG signals from various wearable sensors. The user characteristics, such as the QRS complex, P peak, T peak, etc., were then extracted from the template. After pre-processing the signal, a feature vector is created, and the computing model uses it to determine whether the person is who they claim to be. © 2023 IEEE.

关键词： Electrocardiograms

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Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework 4

Oddity Based Botnet Recognition Utilizing AI Model in the In...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Rani, Rupa Chakraborty, Sudeshna Singh, Pooja Varshney, Neha Abes Institute of Technology Computer Science & Engineering U.P Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India School of Computer Application and Technology Galgotias University UP India Abes Institute of Technology Department of Computer Science & Engineering U.P Ghaziabad India

ISBN: (纸本)9798350393248

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with © 2023 IEEE.

关键词： Cybersecurity

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A Survey on Threat Intelligence Techniques for Constructing, Detecting, and Reacting to Advanced Intrusion Campaigns 18th

A Survey on Threat Intelligence Techniques for Constructing,...

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18th International Conference on Data Science and Artificial Intelligence, ICDSAI 2022

作者： Anand, Ashutosh Singhal, Mudit Guduru, Swapnil Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9783031161773

The rise of intrusion has increased the need for cybersecurity in various organizations. A set of these intrusions by an adversary against a particular organization are called intrusion campaigns. Threat intelligence techniques help detect and respond to intrusion attempts and help organizations set up a framework that can secure their services and interests. This chapter surveys different parameters and resources required to construct such a threat intelligence technique for an organization. Furthermore, the chapter discusses the various cases and models of an Intrusion Detection System (IDS) and Intrusion Response System (IRS) along with their comparison using the security resources collected during the construction of a Threat Intelligence model. All of this combined forms the threat intelligence technique. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intrusion detection

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Enhancement of Alzheimer’s Disease Detection Technique by Fusing Features of Deep Learning and Handcrafted Methods on sMRI Images 3

Enhancement of Alzheimer’s Disease Detection Technique by F...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bhadra, Sampa Rani Maity, Santi Prasad Sil, Jaya Department of Computer Science Kalyani Mahavidyalaya West Bengal Kalyani741235 India Department of Information Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India

ISBN: (纸本)9798331532420

Automated detection of Alzheimer disease (AD) is evolving rapidly with the advent of artificial intelligence (AI). Machine learning and deep learning models. Detection of AD at an early stage provides advance planning of treatment to the patients that arrest progression of the disease. The aim of this paper is to improve performance of diagnosing AD by fusing features extracted mainly from two sources, such as deep and nondeep frameworks applied on the sMRI images. This paper introduces two customized convolutional neural networks (CNN) (baseline model and hybrid model) and extracted two different sets of deep features. As an alternative source of features, a set of handcrafted features are extracted from the sMRI images by applying the stack assembling of Gray Level Co-occurrence Matrix (GLCM) and Gabor algorithms. In order to select nonredundant important features, we apply cosine similarity metric between the handcrafted and deep features. Next, a Feature Pyramid Network-based feature fusion technique namely- FusionNet is also proposed to fuse the deep features and the handcrafted features in order to further improve the classification accuracy. For comparing the performance of AD diagnosis, fused feature sets without feature selection and with feature selection are considered. It has been observed that the fused features without feature selection achieves the best classification accuracy implying that for AD diagnosis all the features are important and therefore distinct. All the experiments have been applied on a 5-class dataset obtained from ADNI and classified by Support Vector Machine classifier. © 2024 IEEE.

关键词： Convolutional neural networks

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Kernel Based Fuzzy Simulation Model for Early Detection of Cancer to Avoid Metastasis 3

Kernel Based Fuzzy Simulation Model for Early Detection of C...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Santra, Soumen Majumdar, Dipankar Mandal, Surajit Deyasi, Arpan Techno International New Town Department of Computer Application Kolkata India RCC Institute of information Technology Department of Computer Science and Engineering Kolkata India B. P. Poddar Institute of Management & Technology Department of Electronics and Communication Engineering Kolkata India RCC Institute of Information Technology Department of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

In this paper, novel cancer detection method using Kernel Based Fuzzy Simulation Model (KBFSM) represented with enhanced accuracy of 12%, considering MRI based breast cancer images. Developed framework, as suggested in this paper, Effectiveness and superiority of the suggested approach are proven by thorough testing on a variety of datasets and thorough assessment metrics, highlighting its capacity to produce accurate and nuanced segmentation results. Fuzzification and defuzzification of images using Fuzzy C means algorithm, speaks in favor of the proposed analysis, as tested over several dataset, owing to minute pixel intensity modification when associated with malignant tissues. Result is supported by histogram analysis, which paves the way of future research in the field of lung cancer using the same technique. © 2024 IEEE.

关键词： Lung cancer

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Towards Full-page Offline Bangla Handwritten Text Recognition using Image-to-Sequence Architecture

Towards Full-page Offline Bangla Handwritten Text Recognitio...

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Ghosh, Ayanabha Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798350314144

In this work, I have developed an Offline Handwritten Text Recognition (HTR) model architecture based on Neural Networks that can be taught to recognise whole pages of handwritten Bangla (Bengali) text without image segmentation. The model is developed in order to aim the extraction of text from an image and accurately sequence it without imposing any limits on the orientation, layout, and size of the text. Bengali being a resource-constrained Indic language, there is a lack of proper annotated dataset consisting scanned images of Bangla handwritten scripts. The publicly available datasets are either not annotated or the annotations are wrong in some cases. Therefore, in this work, I have introduced a new dataset, 'Bongabdo', which consists of full-page handwritten scripts collected from a wide variety of contributors of various age groups, occupation and gender. Significant time has been spent on preparing and modifying the datasets which are currently available publicly. Further, recently proposed State-of-the-art Image-to-Sequence architecture with different settings of hyperparameters have been applied on these images and they have been evaluated in terms of Character Error Rate (CER), Word Error Rate (WER) and Sequence Error Rate (SER) to finally come up with a comparative study. © 2023 IEEE.

关键词： Optical character recognition

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