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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep learning

WellBe: An Intelligent Elderly Care and Well-Being Monitorin...

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Intelligent Technologies (CONIT), international conference on

作者： Atharv Raotole Sailee Sanjay Shirodkar Rahul Shukla Jignesh Sisodia Kailas Devadkar Dept. of Information Technology Sardar Patel Institute of Technology Mumbai India Dept. of Computer Science and Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Deep learning refers to a subset of machine learning that utilizes deep neural networks to simulate the complex decision-making process of the human brain. In recent years, DL has made remarkable progress in addressing multiple unique challenges faced by most elderly individuals. While there have been significant contributions to independent systems catered for the elderly, there is the existence of an overall integrated AI platform that unifies the use of individual modules for an optimized solution. this paper introduces “WellBe,” an innovative AI system that encompasses an alarm-based fall detection component, a facial emotion recognition (FER) system and a built-in personalized chatbot all under a user-friendly website. the novelty of the work extends beyond the aforementioned components, owing to the implementation of an optimized FER system able to handle wrinkles and changes in skin texture, thereby tailored exclusively for the elderly demographic. the system leverages a custom feature extraction module, fusing facial and wrinkle features, in addition to extending a convolutional neural network (CNN) architecture for accurate emotion classification, achieving promising performance with precision, recall, and F1-scores of 0.87, 0.91, and 0.89 for the “Happy” (one of the predicted emotions) class, respectively. the fall detection component, on the other hand, utilizes a pre-trained YOLOv7 Pose model for the real-time identification of falls, sitting at a performance accuracy of 95.3%. Finally, the personalized chatbot is implemented using a rule-based approach enhanced with the Jaccard similarity algorithm, to facilitate prompt assistance and a sense of companionship to the end user. Overall, the integration of these components into a cohesive website offers a comprehensive solution for enhancing the quality of life for elderly individuals living alone, addressing loneliness, safety, and overall well-being.

关键词： Deep learning Accuracy Feature extraction Chatbots Skin Real-time systems Safety

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Research on Intelligent Diagnosis Algorithm of Leakage Current in Low Voltage Distribution Network Based on machine learning

Research on Intelligent Diagnosis Algorithm of Leakage Curre...

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Data Science and computer application (ICDSCA), IEEE international conference on

作者： Renjie Xiao Shijun Luo Bin Li Hui Liao Hai Wang State Grid Jiangxi Electric Power Co. Ltd. Ganzhou Power Supply Branch Ganzhou China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

With the development of smart grids, the security of power systems has become a key issue. this paper discusses a smart leakage current diagnosis method for low-voltage distribution networks. this study mainly uses the BP neural network model, the LightGBM algorithm, and the LightGBM model combined with the IWOA (Imperialist Competitive Algorithm) optimization strategy to conduct leakage current diagnosis. the performance differences between the three are compared and analyzed. the BP neural network simulates the working principle of neurons in the human brain to perform nonlinear mapping and can capture complex data features. the LightGBM is an efficient gradient boosting framework that uses the leaf-wise growth strategy and histogram cache acceleration technology to make the model training more efficient. Furthermore, by introducing the IWOA optimizer to optimize the parameters of the LightGBM model, we have achieved the best results in leakage current diagnosis, improving the accuracy of diagnosis and enhancing the model's generalization ability. the experimental results show that, on the same test set, the IWOA-based LightGBM model has a significant advantage in computational efficiency while maintaining a high prediction accuracy. Although the BP neural network can achieve a higher accuracy in some cases, it has some shortcomings in training time and resource consumption compared to LightGBM. Taking into account the diagnosis accuracy and system operational efficiency, the IWOA-based LightGBM is more suitable for real-time monitoring of low-voltage distribution network leakage fault diagnosis. this study not only provides more reliable leakage fault diagnosis methods for low-voltage distribution networks, but also lays a foundation for further improving the intelligentization level of the grid. Future research directions will focus on improving the algorithm's real-time performance and adaptability to complex grid environments.

关键词： Training Low voltage Accuracy Computational modeling Distribution networks Predictive models Brain modeling Real-time systems Leakage currents Biological neural networks

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A Simulation Modeling Study of Cooperation Evolution Based on Subjective learning and Influence Preference 23

A Simulation Modeling Study of Cooperation Evolution Based o...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Yu Wang Xin He School of Information Engineering Yangzhou University China

ISBN: (纸本)9798400709517

In reality, the behavioral choices of individuals depend on their subjective cognition formed in the process of repeated interactions. the formation of cognition is not only based on the game situation but also influenced by the surrounding individuals. the influence of the surrounding individuals will play a role in the cognition to generate choice preferences. In this paper, we constructed a game model based on subjective game theory and influence preference, explored the co-evolution of subjective cognition and individual influence in structured populations, and investigated the evolution of cooperative behavior. the influence of individuals in the population is heterogeneous. the influence of the surrounding individuals, as one of the key factors, interferes with the formation of cognition, causing individuals to develop preferences in behavioral choices. the mechanism of the influence effect was analyzed by simulation experiments. the existence of influence preference has a significant impact on cooperation. the results show that the size of influence and the magnitude of the increase or decrease in influence are not the greater the better. there is an optimal values, and the relative control over influence can better sustain the cooperation. Compared with the case of no influence mechanism, individuals have a better tendency to learn cooperation in subjective cognition, and constantly tend to choose cooperation. And the system is finally in a relative equilibrium state in which cooperation prevails.

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Deep-Reinforcement-learning-Based Proportional Computation Offloading With User Equipment in Smart Home Edge Computing

Deep-Reinforcement-Learning-Based Proportional Computation O...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Haohang Xie Yunlong Tian Li Niu Miao Wang Yonghua Li School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Qingdao Haier Technology Co. Ltd National Engineering Research Center for Digital Home Network Qingdao China Qingdao Haier Intelligent Home Appliance Technology Co. Ltd Qingdao China Haier Youjia Intelligent Technology (Beijing) Co. Ltd Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

the growing adoption of Internet of things (IoT) applications has resulted in a surge in the offloading of time-sensitive and computationally intensive tasks from IoT devices to mobile edge computing (MEC) servers. In this article, we survey the task partition strategy in a smart home edge computing network with multiple user equipments (UEs) and edge servers (ESs), where each UE runs IoT application and generates computation task periodically. the generated task is allowed to be broken down into several proportions then offloaded to ESs following a task partition strategy designed to minimize both execution latency and energy consumption. We first formulate the task partition problem as a nonlinear programming problem. Subsequently, we introduce an algorithm based on multiagent deep deterministic policy gradient (MADDPG) for UEs to optimize their offloading strategies. In the proposed approach, each User Equipment leverages its local observations as input for an actor network, which generates a task partitioning strategy. Simultaneously, a critic network is trained to enhance the performance of task partitioning. through our simulations, the performance of the proposed algorithm has been confirmed to surpass that of alternative methods in terms of minimizing both delay and energy consumption.

关键词： Surveys Energy consumption Costs Multi-access edge computing Smart homes Partitioning algorithms Delays

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Epilepsy Detection through EEG Signal Classification Using machine learning Methods

Epilepsy Detection Through EEG Signal Classification Using M...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Vivek Deshpande P. Gajendran Varun Ojha Latha B Ishika Soni Arunkumar D T Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

In this exertion demonstrates the application of a deep learning algorithm to the analysis of focal and non-focal EEG data for the finding of epilepsy. this research presents a strategy for automating the diagnosis of epilepsy by making use of a soft computing resource. By using the ANFIS classification technique, focal and non-focal samples may be distinguished. Structures computation, features classification, and features transformation are the three pillars upon which this technique rests. For an electroencephalogram (EEG) data, the Approximate and Directional bands are produced when the suggested NSCT is used. NSDF and Non-Subsampled Pyramid Filter are the two main components of this NSCT. these fall into two categories: low pass, which includes power frequencies, and high pass, which includes high frequency noise. To do this, we construct directed subbands and reroute the high-pass signal using NSDF. Step two of decomposition involves creating high and low frequency sub bands by passing the low pass sub band signal via the Non-Sub Sampled Pixel Frequency (NSPF). the directed sub bands that are created are achieved by retransmitting the high-frequency sub band signal via NSDF. Using the statistical properties obtained from these sub-bands, the ANFIS classification method divides the overall EEG signal into focal and non-focal samples. Applying the proposed method to a collection of EEG signal extraction measurements allows for validation.

关键词： Deep learning Analytical models Time-frequency analysis Sensitivity Noise Epilepsy Pattern classification Brain modeling Electroencephalography Data models

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Analyzing Truck Driver's Behavior on the Road Using YOLO v4 Tiny Algorithm 22

Analyzing Truck Driver's Behavior on the Road Using YOLO v4 ...

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proceedings of the 2022 6th international conference on computer Science and Artificial Intelligence

作者： Felizardo Calimag Reyes Jr. Gerald Tomelden Cayabyab Paula Jean Castro Mendoza College of Information Technology Education Technological Institute of the Philippines Philippines

ISBN: (纸本)9781450397773

the Philippine logistics industry is always in need of competent and qualified truck drivers to meet the increasing demands of the country's economic operations. thus, this research paper investigates the driving competency of truck drivers driving behaviour. the industry plays a critical part in the country's economy, and any trucking accident creates a significant problem for the parties involved that causes a decline in productivity and loss of revenue. this research paper discusses the creation of a system which utilizes machine learning, data analytics, and internet-of-things for easy monitoring and assessment of the truck driver's driving behaviour. In collaboration with the Department of Science and Technology and the Technological Institute of the Philippines, the project aims to integrate various technologies and use it to assess the roadworthiness of truck drivers and use the YOLO v4 algorithm to train the model. It provides all the necessary details covering the statistics needed to assess the truck driver that aligns with the standards set required by the Land Transportation Office (LTO) administration. the Agile methodology was used throughout the development as there are constant changes being made which adds flexibility and puts fewer risks for the system development. Based on the model compiled and the results of the iteration on Yolov4-tiny the classes average precision has a minimum value of 75.45% and a maximum value of 99.89%. the results showed a high accuracy performance of the model created in information retrieval and object detection.

关键词： Truck Drivers Rest APIs IoT Ant Design AWS MERN Stack Web Development Web application

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Detection of Marine Chemical Pollution Based on Image Processing and machine learning 22

Detection of Marine Chemical Pollution Based on Image Proces...

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proceedings of the 2022 6th international conference on Electronic Information Technology and computer Engineering

作者： Lingyun Chen Mingjie Gao Langyu Wang Chuhan Xue Department of Information Beijing University of Technology China Department of Electrical and Computer Engineering Queen's University Canada School of Rail Transportation Soochow University China School of Electric Power South China University of Technology China

ISBN: (纸本)9781450397148

the problem of oil spills is lethal to the ocean ecosystem. To solve the problem, one of the most important key steps is to detect the ocean surface and judge whether there are or not oil spills. Remote sensing provides the advantage of controlling and observing events remotely, and it can cover the areas that people cannot access, so we use it to build a database. Next, we choose to use Matlab for the pre-image processing and then use the neural network by Python to realize and there are five pre-processing methods: expanding the dynamic histogram range in the ‘Y’ channel (method 1), expanding the dynamic histogram range in three channels (method 2), contrast enhancement (method 3), expanding the dynamic histogram range and then contrast enhancement (method 4), and contrast enhancement, and then expanding the dynamic histogram range (method 5). Finally, we use a neural network to test accuracy, in comparison, method 1 is the best and we improve the accuracy from 72% to 82%.

关键词： Contrast Histogram Image processing Neural network

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application of Evolutionary Data Mining Algorithms to Insurance Fraud Prediction

Application of Evolutionary Data Mining Algorithms to Insura...

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the 2012 4th international conference on machine learning and Computing（ICMLC 2012）

作者： Jenn-Long Liu Chien-Liang Chen Dept.of Information Management I-Shou UniversityKaohsiung 840Taiwan Dept.of Information Management Fortune Institute TechnologyKaohsiung 831Taiwan Dept.of Information Engineering I-Shou UniversityKaohsiung 840Taiwan

this study proposes two kinds of Evolutionary Data Mining (EvoDM) algorithms to the insurance fraud *** is GA-Kmeans by combining K-means algorithm with genetic algorithm (GA).the other is MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO).the dataset used in this study is composed of 6 attributes with 5000 instances for car insurance *** 5000 instances are divided into 4000 training data and 1000 test *** different initial cluster centers for each attribute are set by means of (a) selecting the centers randomly from the training set and (b) averaging all data of training set ***,the proposed GA-Kmeans and MPSO-Kmeans are employed to determine the optimal weights and final cluster centers for attributes,and the accuracy of prediction for test set is computed based on the optimal weights and final cluster *** show that the presented two EvoDM algorithms significantly enhance the accuracy of insurance fraud prediction when compared the results to that of pure K-means algorithm.

关键词： Evolutionary Data Mining Genetic Algorithm Momentum-Type Particle Swarm Optimization Insurance Fraud Prediction

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EfficientNet-B0-Based Classification of Pneumonic Lungs Using Chest X-Ray Dataset

EfficientNet-B0-Based Classification of Pneumonic Lungs Usin...

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Intelligent Technologies (CONIT), international conference on

作者： Retinderdeep Singh Neha Sharma Kapil Rajput Mukesh Kumar Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this research paper, the authors take a new angle: classify pneumonic lungs by utilizing up-to-date architecture such as EfficientNet-BO. Given a huge X-Ray dataset of real cases, the model is excellent at identifying pneumonia. the study aims to address a crucial need in the area of respiratory medicine-through development and application for clinical diagnosis of diseases of the lungs which are efficient, accurate and can be used on everyone. Its strong ability to learn is shown as the model demonstrates an awesome 99.50% training accuracy, checks itself out by verifying results with a validation accuracy of 92.20%, and sits tight under pressure during testing with an accuracy at 99.28%. As a matter of fact, these results serve to remind us that this model is excellent in accurately identifying normal and pneumonic lung conditions. this research not only enriches the growing literature on deep learning techniques in medical image analysis but also offers an extremely valuable tool for health care providers which can rapidly and precisely diagnose pneumonia. the method uses the EfficientNet-B0 architecture, known for its high performance in image classification. the model is built from a rich and diverse Chest X-Ray dataset, ensuring that it can be generalized to different patient populations and conditions of imaging. the study’s findings hold promise for improving diagnostic accuracy, lowering human error as well as ultimately transforming patient outcomes in the realm of pneumonia detection by means automated image classification.

关键词： Training Accuracy Pneumonia Lung Data models Ensemble learning X-ray imaging

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A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted machine learning Model

A Robust Development of Calorie Prediction Methodology based...

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Intelligent Technologies (CONIT), international conference on

作者： G Naga Venkata RamaKrishna S Sreelakshmi Meenakshi Diwakar S. Ramkumar T Vinithra Banu T. thilagam Faculty of Engineering University of Technology and Applied Sciences Al Musannah Sultanate of Oman Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of CSE (AI and ML) S.A.Engineering College Chennai Tamil Nadu India Department of Electronics and communication engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering S.A Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Modern people’s hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it’s not hard to keep track of one’s calorie intake. there aren’t many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. this paper presents an innovative ML model for accurate calorie prediction; it’s called the Improved learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we’ll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object’s known volume to determine the food item’s volume. Once the volume has been determined, the food item’s mass may be determined using formulae. then, the connection between weight and calories can be used to compute the food item’s calories.

关键词： Obesity Schedules Accuracy Target recognition Computational modeling Predictive models Feature extraction

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