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

作者： Ilavendhan, A. Paul, Bhenedix Pokala, Joshnavi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331532420

Object tracking has proven to be an essential in a wide range of applications and unmanned aerial vehicles (UAVs) provide advantages to this field in accurately observing and tracking moving targets in challenging environments. This paper presents an overview of various object tracking methodologies which can be utilized on UAVs. It discusses different approaches to attain an improved method of UAVs to help track moving objects accurately, even under challenging conditions such as occlusions or changes in object size and speed. This research study highlights the combinations of traditional tracking techniques infused with modern machine learning techniques to help explore advancements and opportunities of performance improvement. Key topics discussed are the use of algorithms that enable prediction of location of objects, techniques for tracking even small objects in dynamic environments and possibilities of UAVs cooperating with ground vehicles to improve tracking accuracy by sharing data. Overall, the paper discusses the importance of developing more efficient and reliable tracking systems which have applications in various fields such as surveillance, search and rescue operations. © 2024 IEEE.

关键词： Object tracking

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State-of-the-Art of Voice Assistance technology, Mitigating Replay Attacks: A Comprehensive Discussion 5

State-of-the-Art of Voice Assistance Technology, Mitigating ...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sandosh, S. Saxena, Raina Shah, Shashvi Rachiraju, Sai Sahiti School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

In recent years, smart home assistants have become increasingly popular among consumers worldwide. These intelligent devices, powered by artificial intelligence and voice recognition technology, Smart home assistants bring convenience and effectiveness to handling daily activities and overseeing different aspects of our residences. Nevertheless, the expanding use of these assistants raises apprehensions regarding security and privacy due to heightened connectivity and data gathering capabilities. The intensity of security threats associated with these devices is alarming. Specifically, security concerns regarding replay attacks have emerged as a critical challenge. While various strategies have been proposed by researchers in this domain, there is a need for more robust mechanisms. This research paper proposes a novel approach to mitigate replay attacks by combining nonce numbers and timestamps. The proposed use of timestamp and nonce number methods in preventing replay attacks in smart home assistants is highly promising. The proposed solution enhances the security of voice commands transmitted between the user and the smart home assistant. By introducing unique nonce numbers and timestamps in the communication protocol, the system effectively detects and prevents the replaying of previously recorded commands. © 2024 IEEE.

关键词： Authentication

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From Code to Conundrum: Machine Learning's Role in Modern Malware Detection 2

From Code to Conundrum: Machine Learning's Role in Modern Ma...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Jha, Vaibhavi Saxena, Akshat School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350370188

In this research paper, we'll be diving into the combination of malware analysis and machine learning to stepup security. Right now, our digital world is like an anthill: connected in every possible way. In this study, we used machine learning algorithms like Random Forests, K-Nearest Neighbours (KNN), and Logistic Regression for anomaly detection. Inside 60,000 benign and malware instances lie the answers we are looking for. We judge these three algorithms on how well they can detect accuracy, precision, recall, and Fl- score. We hope to give professionals practical insights to secure their systems from malware while constantly being aware of new threats. In a day where security is crucial, this work will connect past issues with machine learning today so that we can better prepare for problems tomorrow. © 2024 IEEE.

关键词： Logistic regression

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Heart Attack Prediction Using Ensemble Learning

Heart Attack Prediction Using Ensemble Learning

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mali, Saurabh Veeramani, Karthika School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Heart attacks are a leading cause of death worldwide, making it a necessity to develop accurate prediction systems for timely intervention and prevention. This paper addresses the challenge of heart attack prediction through ensemble learning methodologies. Previous research has primarily focused on individual machine learning algorithms, often overlooking the potential of ensemble techniques to improve predictive accuracy. In this study, we employed a dataset comprising various clinical features and medical indicators. After preprocessing steps such as encoding categorical variables, scaling, and balancing data using SMOTE, we explored multiple machine learning models, including logistic regression, SVM, KNN, decision trees, random forests, gradient boosting, and artificial neural networks. Additionally, feature selection techniques and hyperparameter optimization were utilized to enhance model performance. Finally, we employed ensemble learning, specifically Stacking, to combine the strengths of individual models, resulting in a higher predictive accuracy of 93%. The results demonstrate the high efficiency of ensemble learning in heart attack prediction, showcasing a significant improvement over individual models. © 2024 IEEE.

关键词： Heart

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Real-Time Implementation of an Automated Student Attendance Monitoring System with computer Vision technology 5

Real-Time Implementation of an Automated Student Attendance ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Lakshmi, M. Vidhya SivaPriya, M.S. Manivannan, Kirankumar Vivekanandan, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350379945

computer vision and the Internet of Things have led to significant advancements in student attendance monitoring. In this work, an innovative system has been designed to facilitate real-time, automated student attendance tracking in educational environments. Utilizing face recognition algorithms and IOT. The aim of the system is to optimize attendance management in educational institutions. IoT-connected, high-resolution cameras are strategically positioned across classrooms to capture visual data for analysis. By utilizing advanced computer vision algorithms, the system identifies and tracks students in real time, facilitating precise and efficient attendance monitoring. Student Faces are detected using a Haar cascade classifier and recognized using the LBPH (Local Binary Pattern Histogram) Algorithm. Then, Histogram data is checked against an established dataset. If a Match is found, the device automatically labels attendance. The Integration of IoT helps in seamless data transmission, storage and ensures accessibility and analysis of attendance records by administrators and educators. The primary objective is to offer an intelligent, automated solution for attendance management, that will significantly reduce manual workload and enhances accuracy. This system addresses the limitations of conventional attendance-taking methods, which are often laborintensive, error-prone, and time-consuming. © 2024 IEEE.

关键词： Local binary pattern

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Human Detection and Tracking for Drone based Marine Surveillance 15

Human Detection and Tracking for Drone based Marine Surveill...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Micheal, Ancy A. Sivaramakrishnan, Sneha School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350370249

Effective object detection and tracking are essential for prompt and precise rescue operations in water-based emergency response scenarios. Acknowledging this necessity, this paper introduces an innovative method using deep learning methodologies. At the heart of this project is the MobDrone dataset, selected to document various situations that occur during water crises. By carefully reannotating a subset of this dataset, new labels of swimmers, rescue victims, and deceased individuals were generated, making it more useful for training and assessing. In this context, the research assesses the effectiveness of YOLOv9 using this specific dataset. By conducting thorough experiments, the study highlights YOLOv9’s exceptional accuracy and resilience, particularly in comparison with its predecessor YOLOv8. This research highlights the possibility of combining advanced object detection algorithms such as YOLOv9 with effective tracking methods like BoT-SORT to transform autonomous systems in water-based emergency response, leading to improved and quicker rescue operations. © 2024 IEEE.

关键词： Object tracking

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Improving Diagnostic Accuracy: A Deep Dive into Random Forest Optimization for Clinical Data 3

Improving Diagnostic Accuracy: A Deep Dive into Random Fores...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Modak, Masooda Jadhav, Madhavi Kadam, Riya Arwade, Manasi Mane, Samiksha SIES Graduate School Of Technology Department of Computer Engineering Nerul India

ISBN: (纸本)9798350372120

The healthcare industry produces a large amount of patient data, which makes it possible to use a variety of analytical techniques on a large dataset. A predictive system that can recognize several diseases at once has been created by utilizing machine learning techniques. Grid Search and Randomized Search strategies are used to further enhance the model that was developed using the Random Forest algorithm. This contrasts with widely used systems that frequently display a numerous illness prediction system without improving the model, producing erroneous and subpar results. Reduced accuracy and erroneous results have serious consequences that could endanger the health of the patient. Our system's present focus is on two different diseases: Diabetes and Parkinson's disease. Especially, the architecture of the system makes it possible to easily incorporate other illnesses in later stages. Users are required to input various disease-specific parameters, after which the system promptly generates output indicating the presence or absence of the specified condition. This research work carried out demonstrates the disease prediction system developed using Optimized Machine learning algorithms such as Decision Tree classifier, Random forest classifier, Support Vector Machine (SVM) and Logistic Regression along with Optimization techniques Grid Search and Randomized Search. The primary objective of the model is to enhance its recall capabilities through the incorporation of advanced methodologies and optimization techniques. The paper also presents comparative study of the results of the above algorithms used. © 2024 IEEE.

关键词： Forecasting

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A Proposal for Deep Learning Based Emotion Identification 13

A Proposal for Deep Learning Based Emotion Identification

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Nayak, Sinkon Pandey, Manjusha Rautaray, Siddharth Swarup School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350380583

Emotion detection plays a vital role in understanding human behavior and enhancing human computer interaction. This study presents the development and evaluation of an Emotion Detection Tool utilizing machine learning techniques. The tool aims to classify emotions from facial expressions in real-time video streams. The methodology involved using convolutional neural networks (CNNs) for feature extraction and emotion classification. A publicly available dataset consisting of facial images indexed with six basic feelings is used for testing the proposed model. The implementation includes real-time video capture, facial landmark detection, and emotion inference. The CNN architecture is explored and compared for their performance in feeling identification. This study uses various evaluation measure parameters to judge the model's strength. The results demonstrate promising accuracy in recognizing basic emotions from facial expressions, with the highest accuracy achieved for happiness and surprise. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Analysis of Machine Learning Algorithms for Covid-19 Detection using Cough Analysis

Performance Analysis of Machine Learning Algorithms for Covi...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Subitha, D. Kavitha, J.C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309140

With an alarming rate of Covid-19 cases worldwide, the challenge for early detection of this disease is growing. There are many methods available for the detection of Covid-19 but they can be tiresome and difficult. The more common method of testing is reverse transcriptase-polymerase chain reaction (RTPCR) testing which requires direct contact with the patient during the testing that further causes more chance for disease spread. But, the method of testing that uses coughing sounds was found to be more safe method of testing as this does not require the presence of patient in the lab rather the pre-recorded audio wave is enough to perform the testing. Machine Learning algorithms are a great promise for detection of Covid-19 disease. There are many different approaches for detection involving wide range of inputs. Likewise, this paper shows about the possibility of detecting Covid-19 using cough sound of the patient, by extracting required input features from the audio file using short time Fourier transform method. Simulation results shows the performance of different classification algorithms in terms of Accuracy, Precision, Recall and F1-Score for the detection of Covid-19. This paper has developed a web application which can detect the presence of corona virus. The audio recording of cough is loaded as the input in to the app which is then sent to deep learning model at the back end that returns the Covid infection result at the front end of the application. This also shows the accuracy of various algorithms in the web application. © 2024 IEEE.

关键词： COVID-19

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food Science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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