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Face Sketch Recognition Using Image Synthesis And Deep Learning

Face Sketch Recognition Using Image Synthesis And Deep Learn...

引用

International Conference on Smart Computing & Communications (ICSCC)

作者： E Poornima K Pragisha Dept. of Computer Science L B S Institute Of Technology For Women Poojapura Trivandrum India

Criminal investigations heavily rely on crime scene sketches. To appropriately categorize sketch photos from crime scenes, this research suggests a deep learning based sketch image classification method. Convolutional Neural Network (CNN) is the algorithm utilized to classify each sketch image in the proposed deep learning based system. This method uses a custom image dataset that includes images of various people’s facial sketches. Moreover, the suggested system takes both color and sketch images. Several systems that automatically recognize subjects shown in sketches based on eyewitness descriptions have been developed recently however, when real-world forensic sketches are used their performance frequently suffers. Despite its effectiveness in many application domains, such as conventional face recognition use deep learning for face photo sketch recognition. This is primarily because there aren’t enough available sketch images to adequately train big networks. In order to address significant issues with the current face recognition system, a deep learning face synthesis and recognition system is proposed in this study. For face identification in the suggested system, the input can be either a color image or a sketch image. Images of various people’s face sketches will be utilized to train the recognition algorithm. The training algorithm is a deep neural network. The system may be able to recognize input face images once training is completed. This system comprises two steps for face recognition and synthesis. If the new input is a sketch image, the recognition stage receives the input right away. The GAN network is used to produce equivalent sketch images if the input is a color image. In this instance, the input to the CNN for classification is a synthesized image. Thus, the proposed system is a combination of image synthesis and deep learning.

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DAAED: A Deep Autoencoder based Adversarial Example Detection Technique

DAAED: A Deep Autoencoder based Adversarial Example Detectio...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Vivek Kumar Manish Kumar Dept. of Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology New Tehri India

Deep Learning based intrusion detection systems are susceptible to adversarial examples which are maliciously perturbed data samples that can cause a trained intrusion detection system to misclassify. The original data sample is generally a malicious data sample which is carefully perturbed to an extent that it retains its malicious properties but, due to perturbations, causes the deep learning-based intrusion detection system to misclassify it as benign. This may have disastrous consequences for security critical systems like intrusion detection systems. In this paper, we propose a deep autoencoder based adversarial example detection technique which effectively detects adversarial examples by analyzing the mismatch between the reconstruction error of an adversarial example, and its predicted class by a trained model. Our experimental results on the five different adversarial example generation techniques: FGSM, Deepfool, PGD, BIM and JSMA, depicts the efficiency of our proposed detection technique with upto 97.8% accuracy and very few false positives. The experiments are performed on CICIDS2017 dataset and depict high detection accuracy of adversarial examples.

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator

Closed-Loop System Identification of an Unstable Nonlinear S...

引用

First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Benjaset Maneeloke Sudchai Boonto Peerayot Sanposh Yodyium Tipsuwan Natthawut Chinthaned Warit Tanaprasitpattana Dept. of Electrical Engineering Kasetsart University Bangkok Thailand Dept. of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Computer Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies.

关键词： Time-frequency analysis Adaptation models Regulators Accuracy Fitting Machine learning Robustness Closed loop systems Complex systems Frequency control

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Exploring CNN Architectures for Seizure Detection from MRI during Pregnancy

Exploring CNN Architectures for Seizure Detection from MRI d...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Geetanjali Nayak Neela Madhab Padhy Tusar Kanti Mishra Dept. of Computer Science and Engineering GIET Unviersity Gunupur Odisha india Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of higher Education Manipal Karnataka India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Deep Learning (DL) represents a subset that has revolutionized traditional machine learning. Unlike earlier methods relying on handcrafted feature extraction, DL automates both feature extraction and classification. This innovation has significantly advanced applications in various medical domains, including the diagnosis of epileptic seizures. This research provides a comparative overview of studies concentrating on automated epileptic seizure detection using leading convolutional neural networks (CNN) architectures. It explores diverse architectures proposed for the automatic diagnosis of epileptic seizures through MRI, encompassing an analysis of rehabilitation systems utilizing DL. The proposed work investigates the application of four primitive CNN architectures on the identification of onset of seizure. The proposed architectures are validated through benchmark MRI samples. Experimental evaluation is made on samples of pregnant females. Overall rate of accuracy stands at 85%, 80%, 90%, and 95% respectively.

关键词： Pregnancy Deep learning Neurological diseases Technological innovation Accuracy Magnetic resonance imaging computer architecture

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EU-India Collaboration for Smarter Microgrids: RE-EMPOWERED project

EU-India Collaboration for Smarter Microgrids: RE-EMPOWERED ...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Kotsampopoulos, Panos Dimeas, Aris Chronis, Alexandros Saridaki, Georgia Hatziargyriou, Nikos Maiti, Suman Chakraborty, Chandan National Technical University of Athens School of Electrical and Computer Engineering Athens Greece Indian Institute of Technology Kharagpur Dept. of Electrical Engineering Kharagpur721302 India

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Recent technical, economic, social and environmental challenges related to global energy transition have gained increased interest from research institutes and industrial companies from all across the world. RE-EMPOWERED is a joint research program, in which European and Indian partners have joined forces to demonstrate innovative solutions for effective energy management, power forecasting techniques, added grid resilience, grid stable operation, energy sector integration, digitalization and power electronics control. The developed tools will be tested in four demo sites in Europe and India aiming to shed light on these timely research areas in the reign of power networks dominated by distributed and renewable sources generation. © 2022 IEEE.

关键词： Energy efficiency

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MediVault DApp: A Electronic Health Record Application Using Blockchain technology 2

MediVault DApp: A Electronic Health Record Application Using...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Peddu, Lingeswara Reddy Mishra, Aditya Padmaja, Amirineni Rama L Rekha, G. Palanikumar, S. Pongiannan, R.K. SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur603 203 India Geethanjali College of Engg. And Tech. Department of ECE Hyderabad India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engg. College Avadi Dept. of Computer science and business systems Tamilnadu Chennai India

ISBN: (纸本)9798331505745

The healthcare industry stands to benefit greatly from blockchain technology due to its robust security, privacy protection, confidentiality, and decentralized nature. This study outlines how blockchain can revolutionize current medical data systems and proposes a framework for its implementation. The primary objective of this framework is to ensure secure storage of electronic records by establishing comprehensive access protocols for users. When patients seek medical care, hospitals generate and store patient records in a decentralized application (DApp), which is a blockchain- based platform for maintaining electronic medical records. Medical professionals can modify patient records, while patients can retrieve their medical information from the DApp using public keys. Electronic Health Records (EHRs) allow healthcare providers to monitor information necessary for compliance with insurance companies and government regulations. EHRs serve as a centralized data repository for storing doctors' orders related to laboratory tests, x-rays, and other diagnostic procedures. The proposed system aims to optimize individual medical information management, minimize redundant testing, reduce treatment delays, and equip patients with adequate information to make informed decisions. © 2025 IEEE.

关键词： Electronic health record

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Text-independent Speaker Recognition: A Deep Learning Approach

Text-independent Speaker Recognition: A Deep Learning Approa...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Arman Shirzad Ali Nasiri Razieh Darshi Zohreh Safarpour Razieh Abdollahipour Dept. of Computer Engineering University of Science and Culture Tehran Iran Dept. of Electrical and Computer Engineering University of Tabriz Tabriz Iran Dept. of Electrical Engineering Iran University of Science and Technology Tehran Iran Dept. of Electrical Engineering Berlin University of Applied Sciences Berlin Germany Dept. of Sensors and Modeling Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this study, we introduce an artificial neural network (ANN) model specifically designed for speaker recognition and compare its performance and accuracy with that of the established Keras speaker recognition model. Our investigation utilizes two distinct datasets: the first comprises voice recordings in three different languages from both male and female speakers; the second is the widely recognized LibriSpeech dataset, a comprehensive corpus of English speech derived from 50 individuals reading audiobooks. The proposed model leverages the Fast Fourier Transform (FFT) to analyze the input data, which consists of labeled .wav files. These files undergo a preprocessing stage before being input into the ANN for training. Our results indicate that while the proposed model outperforms the Keras model on the first dataset, it exhibits marginally lower performance on the LibriSpeech dataset. This finding suggests that even well-established models like those provided by Keras may not universally deliver optimal results across different datasets.

关键词： Training Analytical models Accuracy Fast Fourier transforms Computational modeling Speech recognition Artificial neural networks Data models Speaker recognition Recording

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Optical Character Recognition for Urdu Text: A Review of Techniques, Challenges, and Future Directions

Optical Character Recognition for Urdu Text: A Review of Tec...

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International Conference on Advances in Electronics, computers and Communications (ICAECC)

作者： Moinuddin Ahmed Shaik Wahid Abdul Dept. of Computer Science and Information Technology Maulana Azad National Urdu University Hyderabad India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

In recent years, Urdu Optical Character Recognition (OCR) systems have received considerable attention due to the increasing demand for automated recognition and analysis of text, which requires the development of robust OCR systems to convert printed and handwritten Urdu text into digital format. This article aims to provide a comprehensive overview of the most recent advances in OCR systems for Urdu. Analysing and synthesising 30 relevant research articles published between 2018 and 2023 to determine their contributions and future directions. Numerous studies have explored the use of deep neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), to achieve high accuracy in character recognition by incorporating evolutionary algorithms, transfer learning, augmentation, and holistic unconstrained recognition systems. These models have demonstrated promising offline and online Urdu handwriting recognition results. The reviewed articles also shed light on the difficulties and limitations of existing Urdu OCR systems, such as the lack of standardised datasets, the dearth of annotated training data, and the need for robustness against noise and variations in handwriting styles. The literature discusses the development of more efficient algorithms, the investigation of multi-domain approaches, and the integration of deep learning techniques with other image processing techniques as future directions. The findings of this review will help researchers and practitioners in understanding the most recent techniques and challenges in Urdu OCR systems.

关键词： Deep learning Handwriting recognition Accuracy Recurrent neural networks Reviews Optical character recognition User interfaces

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Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

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Innovative Engineering Sciences and Technological Research (ICIESTR), International Conference on

作者： Kanimozhi Kannabiran B. Raja Mohamed Rabi Booma Jayapalan Krismadinata Thangapalani Lakshmanan Rajalakshmi K Dept. of Electrical and Electronics Engg. NPR College of Engineering & Technology Natham India Dept. of Mechanical Engineering Rathinam Technical Campus Coimbatore India Dept. of Electronics and Communication Engg. PSNA College of Engineering and Technology Dindigul India Centre for Energy and Power Electronics Research Universitas Negeri Pedang Indonesia Dept. of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India Dept. of Computer Science and Engineering NPR College of Engineering & Technology Natham India

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Stability of power electronic converters is one of the most important problems related to their dynamics. The problems related to global asymptotic stability is discussed here. The design example used is DC-DC buck converter with Adaptive sliding mode control. An averaged small signal model is derived and transfer functions are obtained. Stability is also verified using conventional techniques by MATLAB simulation. Simulation results and mathematical proof shows that closed loop system designed is stable and a fast response is obtained. Simulated results show that the simulated converter system acquires the excellent robustness feature of achieving a stable steady-state and fast transient response under variable operating points. A prototype model has been developed and simulated results are compared with experimental results. The results validate the stability analysis of Dc-Dc buck converter

关键词： Asymptotic stability Transient response Buck converters Simulation Prototypes Stability analysis Mathematical models Robustness Steady-state Load modeling

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Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction

Integration of Swarm Intelligence with KNN for Optimal Neare...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Aniketh Anchalia Anirudh Kakati Ambika Patil B N Bhavana Ankit Paudel Shilpa Chaudhari Dept. of Computer Science and Engineering MS Ramaiah Institute of Technology (Affiliated to VTU) Bengaluru India

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction.

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