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Chatbot - Attendance and Location Guidance system (ALGs) 3

Chatbot - Attendance and Location Guidance system (ALGs)

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3rd international conference on signal processing and Communication (ICPSC)

作者： Sweena, Kripa S. Lisa, Margreat R. Shiny, Priscilla C. Sancy, Swetha P. Paul, J. John Thusnavis, Bella Mary, I Karunya Inst Technol & Sci Dept ECE Coimbatore Tamil Nadu India

ISBN: (纸本)9781665428644

Well, Timely prediction and decision making are most indispensable in this modern era of internet of things (IoT). In this paper we propose a prototype of a CHATBOT for attendance and location guidance system (ALGs) using a raspberry pi-based computer. The proposed prototype autonomously marks attendance for the registered/known students and a guidance system for unknown students/parents in a school or university. The CHATBOT includes a webcam with Haar-Cascade classifier using OpenCV machine learning cross platform library for face recognition and a Google API for speech recognition. This speech recognition API converts the voice from the microphone into written text (speech into text) and vice versa. The prototype also includes an additional switch module, preferably when the user's microphone fails or unable to detect the user's input voice. The switch module comes with a set of 6 push button switches with predefined questions for each switch. The prototype is tested and provides good results for all the different use cases.

关键词： OpenCV Raspberry pi 3 b+ Google API CHATBOT Face recognition Voice recognition

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An Efficient Face Recognition Method Based on CNN

An Efficient Face Recognition Method Based on CNN

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Xiu He Feng Ding Guangzhou Xinhua University Guangzhou China

As a prevailing research in artificial intelligence, the application of computer vision is widely used in many fields which are closely related to people's livelihood, such as industrial automation, new retail industry, smart transportation and security monitoring. And the proposed face recognition method is a branch in the field of computer vision, it integrates neural networks, biology, image signal processing, machine learning and other fields, which promote research and cross-development among different disciplines. Hence, this paper focuses on face recognition method by using convolutional neural network(CNN), and CNN has the property of "weight sharing", which has been widely popularized in image recognition, it can greatly simplify the work of large-scale network training. The experiments demonstrate that the proposed face recognition method is successful, and the accuracy of the proposed method can be as high as 98%.

关键词： Training Computer vision Image recognition Image color analysis Face recognition Color signal processing

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PDF Malware Detection optimisation using machine learning

PDF Malware Detection optimisation using machine learning

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Rasha Al-Taani Razan Bassah Nisma Naimat Ammar Odeh Department of Computer Science/Cybersecurity Princess Sumaya University for Technology (PSUT)

Initial penetration is an essential technique for launching corporate cyberattacks. In most cases, such invasions result in the loss of private and sensitive data. Because email communication is vital in day-to-day business activity, attackers generally use email as an attack vector to accomplish early penetration of the targeted firm. Emails allow the attacker to deliver malicious files or links to malicious websites. Attackers use social engineering to induce victims to open malicious emails, download files, or click links. Recent assaults often attach non-executable files since existing company defences restrict executables from accessing corporate networks via emails, particularly PDF files. Therefore, there is a high demand for a method that can identify malicious PDF files, as embedded malware in PDFs is a significant hazard to all users nowadays. For many years, static and dynamic processing have been used in malware detection. It employs machine learning techniques to reduce manual input and optimise processing time. In this work, we apply machine learning techniques and multiple machine learning methodologies to identify dangerous programs and evaluate the accuracy of the acquired findings. We obtained greater accuracy when compared to the related studies.

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An Iterative Posterior Regularized NMF-Based Adaptive Wiener Filter for Speech Enhancement 3rd

An Iterative Posterior Regularized NMF-Based Adaptive Wiener...

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3rd international conference on machine learning, Image processing, Network Security and Data Sciences, MIND 2021

作者： Yechuri, Sivaramakrishna Vanambathina, Sunny Dayal School of Electronics Engineering VIT-AP Amaravati India

ISBN: (纸本)9789811958670

In this paper, a novel single-channel speech enhancement algorithm based on an iterative posterior regularized nonnegative matrix factorization (NMF)-based adaptive Wiener filter is proposed. The statistical-based approaches’ performance is poor under the non-stationary background noise conditions. Prior information is needed for template-based methods, but performance-wise template-based methods are robust and better in presence of non-stationary compared to statistical model-based approaches. To overcome the limitations of both, we propose a method that combines the iterative posterior regularized NMF and an adaptive Wiener filtering method. In the proposed method, prior distributions are used for transformed domain spectral magnitudes of speech and noise components to model an iterative posterior NMF. The magnitude of the spectral component of speech and noise is considered as an inverse gamma, Gaussian distribution. The samples of speech and noise are well fit in those distributions to provide a better adaptive estimation of the necessary statistics of those distributions to get a natural regularization of the NMF criterion. An adaptive Wiener filter technique derives the adaptive factor (α) based on the signal-to-noise ratio (SNR) level for the Wiener filter gain. The adaptive factor minimizes the estimation errors of the Wiener filter and improves the speech quality by adjusting the base vector weights based on estimated noise and speech levels. The proposed method outperforms other benchmark algorithms in terms of the source to distortion ratio (SDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： signal to noise ratio

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Feature Extraction from Plant Leaves and Classification of Plant Health Using machine learning 3rd

Feature Extraction from Plant Leaves and Classification of P...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Abisha, A. Bharathi, N. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus No.1 Jawaharlal Nehru Rd Tamil Nadu Vadapalani India

ISBN: (纸本)9789811908392

Feature extraction (FE) is an important method that contributes dimensionality reduction especially when extracting the features from images. It is the method of converting the input images into a set of features understandable by the system or software. While processing images, it is desirable to extract features that are oriented toward discernment between two or more classes. In this proposed method, two classes are considered as infected and healthy, and data is distributed into six cases. Features are extracted from the plant images using feature descriptors Hu moments, Haralick texture and color histogram. After feature extraction, the images are classified as infected or healthy based on the feature descriptors. The extracted data are trained with the various classifier models such as LR, LDA, KNN, CART and RF, and validation is performed. 10 k cross-validation is used, and the three best algorithms are chosen to classify and predict the model. Among these models, RF, CART and KNN have given better performance, respectively. Finally, accuracy score above 95% and r2 score above 85% is found for various cases, and the results are visualized for all six cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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A machine learning Model on Healthcare Based Chatbot and Appointment System

A Machine Learning Model on Healthcare Based Chatbot and App...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Kushagra Srivastava Trilok Nath Pandey Debaraj Roy Sambit Sahoo School of Computer Science and Engineering Vellore Institute of Technology Chennai India

This paper discusses the clinical chatbot which could examine the contamination and deliver essential insights regarding the contamination previous to counseling a specialist. To lower the healthcare charges and similarly increase openness to clinical facts the clinical chatbot is constructed. Certain chatbots pass approximately as clinical reference books, which facilitates the affected person to discover approximately their contamination and assists with running on their fitness. The person can accomplish the real gain of a chatbot simply while it is able to examine all forms of infections and deliver essential data. In later sections of the paper, a doctor appointment system software is also discussed that is integrated with the chatbot so that the specialist which is recommended by medical chatbot can be easily visited at the mutually agreed time of both specialist and patient. Such an appointment system has its own advantages like it reduces the waiting time of the patient, patients can choose the appointment time in keeping with their desire additionally to be had and booked slots are proven in powerful graphical person interface.

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Analysis of machine learning Model-Based Cardiovascular Disease Prediction 3rd

Analysis of Machine Learning Model-Based Cardiovascular Dise...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Smita Kumar, Ela CSE Indira Gandhi Delhi Technical University for Women New Delhi India

ISBN: (纸本)9789811908392

Around the world, cardiovascular disease is the leading cause of death. Cardiovascular disease prediction is a big goal in the world of medical science when it comes to clinical data analysis. The prediction of the heart disease model is examined with various combinations of characteristics, and several needful classification techniques have been considered. In addition to grid search CV, majority voting ensemble classifier has considered enhanced performance level concerning accuracy for the heart disease diagnosis. Implications of data pre-processing techniques are important to manipulate datasets structure plan to the fast and accurate result of cardiovascular disease prediction. In this paper, author had been used the statistical pre-processing method Z-score normalization is the process of rescaling the features. For precise and crisp analysis, various machine learning techniques have been studied. Three computational techniques had been used, namely K-nearest neighbor (KNN), and support vector machine (SVM), stochastic gradient descent (SGD), and random forest (RF) is used for the first stage of implementation after that comparison to grid search CV with all four classification model and finally incorporate ensemble classifier for result analysis. The result shows that the ensemble model and random forest with grid search CV produce a high accuracy of 87% on given datasets. The heart disease dataset is taken from the department of cardiology, Excelcare Hospitals, Guwahati, Assam, for the analysis of performance in each technique which has been evaluated. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cardiology

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signal processing Grand Challenge 2023 – E-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients

Signal Processing Grand Challenge 2023 – E-Prevention: Slee...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kleanthis Avramidis Kranti Adsul Digbalay Bose Shrikanth Narayanan Signal Analysis and Interpretation Lab University of Southern California Los Angeles CA

This paper presents the approach and results of USC SAIL’s submission to the signal processing Grand Challenge 2023 – e-Prevention (Task 2), on detecting relapses in psychotic patients. Relapse prediction has proven to be challenging, primarily due to the heterogeneity of symptoms and responses to treatment between individuals. We address these challenges by investigating the use of sleep behavior features to estimate relapse days as outliers in an unsupervised machine learning setting. We extract informative features from human activity and heart rate data collected in the wild, and evaluate various combinations of feature types and time resolutions. We found that short-time sleep behavior features outperformed their awake counterparts and larger time intervals. Our submission was ranked 3rd in the Task’s official leaderboard, demonstrating the potential of such features as an objective and non-invasive predictor of psychotic relapses.

关键词： Heart rate machine learning Feature extraction Acoustics Behavioral sciences Data mining Task analysis

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Study of Various machine learning Algorithms for use with Automatic Speech Recognition 3

Study of Various Machine Learning Algorithms for use with Au...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Kumar, T. Sathies Sheela, T. Arulselvam, D. Premalatha, S. Srividya, K. Sri Sairam Engineering College Department of EIE Chennai India Vinayaka Mission's Kirupananda Variyar Engg. College Department of ECE Salem India Sri Sairam Engineering College Department of EEE Chennai India

ISBN: (纸本)9781665462754

Speech recognition enables which the system recognize and identify the words from aspeech. It aids in balancing technology enabled Human computer interaction (HCI) and Human robot interaction(HRI). Command and Communication of any information is brought down through Speech. Artificial intellingence carried out with the help of circuit involving speech recognition which involved in Controlling and commanding application such as security system, VCR systems. Speech processing make life easier allows user to perform parallel tasks with increased efficiency and effectiveness(i.e., if the user's hands and eyes are occupied elsewhere OR disabled person who are not using their hand). Occasionally the speech signal is not clearly understand by the system which we communicate with them, by solving this issue we made an various investigation. In this paper we give report of investigate the various methods, algorithm which used in the previous speech recognition technique and system to improve the communication between a Human-Computer Interaction and Human Robotic Interaction to obtain a better results. Various algorithms like dynamic time warping (DTW) and Mel frequency Cepstral coefficients (MFCC), Artificial Neural Network (ANN) and Power Normalized Cepstral Coefficients (PNCC) were analyzed. © 2022 IEEE.

关键词： Neural networks

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Hybrid Deep learning and Ensemble Methods for Alzheimer’s Detection from EEG signals

Hybrid Deep Learning and Ensemble Methods for Alzheimer’s D...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Mokara Anjali G. Naveen Sundar Yetukuri Gana Bala Meghana Beereddy Sreeja Narmadha Senthil Kumar Thangavel Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of AIML Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Alzheimer’s disease (AD) is a progressive neurological disorder characterised by aberrant behaviour, memory loss, and cognitive impairment. Electroencephalography (EEG) is an efficient method that provides valuable information on brain activity and can be used to predict AD. The application of EEG data for AD classification is examined in this abstract, with particular attention paid to the study of frequency bands, and machine learning (ML) techniques. This algorithms classifies AD with excellent levels of accuracy, indicating possibility of EEG-based methods for early disease identification. In order to classify AD in its early stages, this research proposes a unique deep learning model based on convolutional neural networks (CNNs) and signal processing techniques. The proposed model aims to classify patients into two categories: AD positive and negative. At the early stage the dataset was preprocessed using techniques such as label encoding and noise removal. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) are the techniques applied in signal processing. The classification is done using Deep learning (DL) and machine learning (ML) methods are used to develop the best model. ML algorithms namely Stacking classifier, Gradient Boosting, Stacknet, AdaBoost, and DL algorithms like Convolutional Neural Networks (CNN) and Deep Neural Network (DNN) architectures have shown promise in AD classification. Random Forest and AdaBoost excel in ensemble learning, while CNN and DNN models capture intricate patterns in EEG data, further enhancing the accuracy of diagnosis. In conclusion, EEG data offer non-invasive and economical method for classification of AD and contain useful information.

关键词： Deep learning machine learning algorithms Accuracy signal processing algorithms Brain modeling Electroencephalography Classification algorithms Convolutional neural networks Ensemble learning Alzheimer's disease

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