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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Eswar Reddy, R. Santhi, K. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031488870

The aim of this study is to investigate mental fatigue using the Kaplan-Meier and Nelson-Aalen estimators in survival analysis. Mental fatigue is a common occurrence when the mind becomes tired from regular tasks, and it can have a negative impact on an employee’s operational functions and job efficiency. To detect mental fatigue, the shallow Kaplan-Meier method is employed by analyzing data from employee burnout evaluations. Both the Kaplan-Meier and Nelson-Aalen estimators have proven to be effective in automatically analyzing various features from raw data. However, they often impose a significant burden on system resources during training and predictions. Therefore, alternative methods of analysis are necessary to derive the survival curve. In this paper, we provide a mathematical foundation for the Kaplan-Meier method and explain the concept of censoring, including right censoring, interval censoring, and left censoring. Furthermore, we construct a Kaplan-Meier survival curve, which represents the probability of survival over time. The Kaplan-Meier survival curve is considered the most reliable and is recommended for predicting the variable under investigation, particularly in the fields of public health and medical research. The findings of this research can also be utilized to develop interventions and strategies aimed at reducing mental fatigue and improving employee morale. Enhancing employee morale can positively impact an organization as a whole, as mental fatigue has been associated with lower job satisfaction and an increased likelihood of employee turnover, both of which can be further explored in future studies. Overall, this study sheds light on the significance of understanding and addressing mental fatigue in the workplace, and it provides valuable insights that can contribute to the well-being of employees and the success of organizations. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Job satisfaction

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Deepfake Classification for Human Faces using Custom CNN 7

Deepfake Classification for Human Faces using Custom CNN

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Kalemullah, Amaan M. Prakash, P. Sakthivel, V. Vellore Institute of Technology Chennai School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

This paper emphasizes the urgent need for effective deepfake classification methods, particularly for human faces, due to the escalating threat of this technology. The research proposes a comprehensive approach using a Convolutional Neural Network (CNN) model and two Transfer Learning models (ResNet-50 and EfficientNet B7) to address this challenge. It investigates the synthesis of realistic-looking facial manipulations and their societal impacts, highlighting the importance of accurate classification in mitigating these effects. The study evaluates and compares the proposed models' accuracy in detecting manipulated facial content, analyzing their strengths and limitations. Overall, the paper provides a timely exploration of deepfake classification, offering practical solutions to enhance digital security and trustworthiness. © 2024 IEEE.

关键词： Convolutional neural networks

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Unmanned Aerial Vehicles and Low-Flying Drones: A Review of Classification 2

Unmanned Aerial Vehicles and Low-Flying Drones: A Review of ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pagrut, Shrijeet Jawandhiya, Pradip Shri Sant Gajanan Maharaj College of Engineering Department of Computer Science and Engineering Maharashtra Shegaon India Pankaj Laddhad Institute of Technology and Management Studies Department of Computer Science and Engineering Maharashtra Buldhana India

ISBN: (纸本)9798331528713

Nowadays Research and Development of Novel and Efficient drones becomes the core part of strategic practices of every nation. There is a growing need of using a variety of Unmanned Aerial Vehicles with significant capabilities in Civilian, Military and Commercial Applications. UAVs flight patterns such as Vertical Takeoff Landing and Horizontal Takeoff Landing, its maximum altitude capacity, range are discussed. Designing of Low Flying drones are classified into Small, Micro and Nano-Pico-SmartDust(NPSD) categories. Use and Impact of different materials like Titanium alloy in design is provided. Additionally, current activities in the market related to small drones and future research direction in the field of utility and applications drones are discussed. © 2024 IEEE.

关键词： Drones

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Transforming Traditional Medical Appointment Booking with Advanced Technologies and User-Centric Design 3

Transforming Traditional Medical Appointment Booking with Ad...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mahanthi, Avinash Naidu Jacob, T.Prem Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350322842

Life gets too busy to go to a doctor in character and keep up with the right hospital therapy. The most important motive of this work is to offer patients quite simple and luxurious experience during the appointment with the physician, as well as to addressing the questions that the sufferers provide during the appointment. The Find-My-Doctor acts as a client, and is maintained with the aid of an internet site that acts as a server and at the same time a database containing medical records, doctors, patients, and appointment statistics. The present-day widespread working procedures for registering patients and making appointments are time-eating and extremely cumbersome. Find-My-Doctor is an internet-based application designed to streamline the appointment procedure for more than one scientific/healthcare institution. © 2023 IEEE.

关键词： Appointment Booking System Database Security Full Stack Application MongoDB NodeJS ReactJS

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Crime Prediction by Comparing Machine Learning and Deep Learning Algorithms 2

Crime Prediction by Comparing Machine Learning and Deep Lear...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Malik, Kush Pandey, Manas Khan, Asma Srivastav, Manvesh Gera, Anju Chauhan, Sansar Singh G L Bajaj Institute of Technology and Management Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Preventing crime is important for justice and safety in cities. Using computers to predict crime trends can help make cities safer. Reliable real-time crime prediction is necessary for public safety, but there are still difficulties facing science. Crime rates are influenced by numerous intricate factors. Crime is low in comparison to many predictable events. For computer systems, determining the criminal activity rate, kinds, and hotspots based on historical patterns is both a challenge and an opportunity. The accuracy of machine learning using SVM stacking, KNN, Naïve Bayes, Random Forest, and deep learning using LSTM are compared in this study. This paper presents a comprehensive review of the literature on deep learning and machine learning approaches to crime prediction. It's an important tool for scholars studying the subject because it provides information that law enforcement organizations can use to improve how they prevent and deal with criminal activity. Moreover, the system outlined in the paper offers predictions about potential future crimes, allowing for proactive measures to be taken to prevent them. © 2024 IEEE.

关键词： Crime prediction deep learning KNN LSTM machine learning Naïve Bayes Random Forest SVM stacking

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Artificial Intelligence Techniques for Monuments Identification

Artificial Intelligence Techniques for Monuments Identificat...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Surya, U. Subashree, E. Shobana, G. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319019

Effective management is essential to the conservation of historic sites, especially when it comes to the selection of monuments. Using the advanced You Only Look Once (YOLOv8) paradigm, this research study ushers in the era of Artificial Intelligence (AI) by achieving pinpoint identification. The goal is to create an AI-powered device that continuously monitors and provides real-time data on accuracy. Focusing its coverage primarily on Tamil Nadu monuments and a variety of diverse historical settlements located around the Indian subcontinent, the compiled dataset is evidence of this, producing an impressive mean Average Precision (mAP) score of 91.5%. The exposition foreshadows significant advancements in monument detection by providing comprehensive insights as well as introducing a voice component. continuation and historical research. The successful application of AI to monument recognition has significant implications for tourism, urban planning, and cultural preservation. It provides a useful tool for the precise and quick designation and preservation of various monuments. In summary, the goal of the project is to push the boundaries of antiquarian research and cultural preservation. © 2024 IEEE.

关键词： Artificial intelligence

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Experimental Thermal Model-Based SOC Estimation Algorithm for Lithium-Ion Phosphate Batteries Considering Internal Temperature

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Transactions of the Korean institute of Electrical Engineers 2024年第12期73卷 2289-2296页

作者： Kim, Dong Hwan Ko, Jeong Soo Kim, Do Hyeon Lim, Jong-Hun Lim, Je Yeong Lee, Jaein Lee, Byoung Kuk Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Advanced Defense Science & Technology Research Institute Agency for Defense Development Korea Republic of

This paper proposes a method for estimating the State of Charge (SOC) of lithium iron phosphate (LFP) batteries based on an experimental thermal model that considers internal battery temperatures. The proposed algorithm involves selecting a thermal model and suggesting methods for extracting parameters from the chosen model. Using the proposed thermal model, the algorithm compensates for temperature differences in cold and hot environments, thereby correcting the internal temperature to estimate SOC accurately. The effectiveness of the proposed algorithm is verified by estimating the SOC of electric vehicle LFP batteries and comparing the accuracy before and after correction. Copyright © The Korean institute of Electrical Engineers.

关键词： State of charge

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Blind spot monitoring for car 4th

Blind spot monitoring for car

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4th Annual International Conference on Data science, Machine Learning and Blockchain technology, AICDMB 2023

作者： Ranjan, Aditya Pandey, Ravi Shankar Department of Computer Science and Engineering Birla Institute of Technology Patna Campus India Mesra India

ISBN: (纸本)9781032426853

Blind spot of a car is an area around the vehicle that cannot be seen while looking either forward or through side or rear view mirror. This paper introduces blind spot monitoring for vehicles using vehicle mounted cameras in order to reduce traffic accidents. We firstly describe how intelligent surveillance can be used to detect vehicle blind spots by using multiple cameras. We then describe the importance of blind spot detection including technical background. This paper provides an algorithm for vehicle detection in blind spot using Mean shift algorithm based on machine learning. In this approach, a target window is defined in an initial frame for a moving target in a video, and after that, the tracked object is separated from the back ground by processing the data within that window. After that alert message will be displayed on the screen. © 2024 The Author(s).

关键词： Vehicles

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Predicting and Navigating Environmental Transformations using Machine Learning 2

Predicting and Navigating Environmental Transformations usin...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Lakshin Archana, A. Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sea level

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A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care 3rd

A Novel Smartphone-Based Human Activity Recognition Using De...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data sciences, MIND 2021

作者： Soni, Vaibhav Yadav, Himanshu Semwal, Vijay Bhaskar Roy, Bholanath Choubey, Dilip Kumar Mallick, Dheeresh K. Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal India Department of Computer Science and Engineering Indian Institute of Information Technology Bihar Bhagalpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811958670

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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