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WellBe: An Intelligent Elderly Care and Well-Being Monitoring System Using Deep Learning 4

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

引用

4th International Conference on Intelligent Technologies, CONIT 2024

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

ISBN: (纸本)9798350349900

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. © 2024 IEEE.

关键词： Convolutional neural networks

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A Self-Supervised Semantic Segmentation Method for Identifying Barrett' s Esophagus in Endoscopic Images 24

A Self-Supervised Semantic Segmentation Method for Identifyi...

引用

6th Asia Pacific Information technology Conference, APIT 2024

作者： Lin, Guo-Shiang Chang, Ku-Yaw Hsu, Wen-Teng Yang, Sheng-Chih Yan, Shen-Lei Dept. of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taiwan Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan Dept. of Internal Medicine Chang Bing Show Chwan Memorial Hospital Taiwan

ISBN: (纸本)9798400716218

Barrett's esophagus is considered a precancerous condition that may lead to esophageal cancer. The condition is usually diagnosed through an endoscopy with biopsy. This careful imaging examination is quite labor-intensive and usually lacks of diagnostic consistency. In this paper, a computer-aided diagnosis (CAD) method was proposed to assist pathologist in Barrett's esophagus diagnosis from endoscopic images. The proposed semantic segmentation was built based on the U-Net architecture, which features capturing detailed spatial information and context to produce accurate pixel-wise predictions. An autoencoder, which is quite capable of learning a compact and meaningful representation of the input data, is incorporated to achieve self-supervised learning. A two-stage pretraining for the encoder and the decoder is adopted for better performance and reduced data requirements. The proposed method can extract features and patterns from unlabeled data without requiring human annotation or labels, which is very suitable in many biomedical image analyses. The experimental results show that the segmentation accuracy with mean pixel accuracy, mean dice coefficient, and mIoU reaches 98.20%, 87.96%, and 83.18% respectively, indicating that the proposed method has a good performance on the identification of Barrett's esophagus in endoscopic images. © 2024 Owner/Author.

关键词： Semantic Segmentation

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IoT Based Smart Parking System 5

IoT Based Smart Parking System

引用

5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Izudheen, Sminu Gokul, P.V. Joseph, Jesbin Nair, Karthik S Manu Krishnan, N.S. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350334128

The number of vehicles being used for daily commute within the cities have increased exponentially since 2015. This has caused a major concern regarding parking spaces especially in a rapidly developing cities. More than 25% of vehicles driving around the cities are looking for a parking space. Numerous issues arise as a result of poor parking infrastructure such as significant wastage of time for drivers, traffic congestion and increase in car emissions. Implementing smart technology to facilitate this task will solve this problem, improving operational efficiency, simplifying the flow of urban traffic and offering drivers a more enjoyable and time-saving experience. The proposed IoT-based smart parking system allows real-time data about parking availability to be obtained via a mobile application. An IoT gadget, which includes sensors and microcontrollers is placed in each parking space. The data from the gadget will be pushed to a cloud-based system which analyses this data to calculate the availability of free slots in parking spaces. The users receive real-time information on the availability of all parking spaces giving them the choice to choose the best one. © 2023 IEEE.

关键词： Traffic congestion

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Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using Machine Learning Algorithms and Signal Processing Techniques

Unmasking Frame Duplication: Comprehensive Approach to Detec...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Praddipraj, N.N. Praveen Kumar, B. Sreeji, S. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity. © 2024 IEEE.

关键词： Learning algorithms

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Parella, Teja Ponnam, Balakrishna Santhiya, P. Praveen, A. Sujihelen, L. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers. © 2024 IEEE.

关键词： Anomaly detection

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Enhancing Blind Communication With Voice-Assisted Email 4

Enhancing Blind Communication With Voice-Assisted Email

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sasikala, C. Rupalakshmi, M. Sameer Ahammad, S. Meghana, U. Harshad Ali, M. Srinivasa Ramanujan Institute of Technology Dept. Computer Science and Engineering Anantapur India Anantapur India

ISBN: (纸本)9798350385793

Considering the key role of the internet in daily life, accessibility becomes crucial, especially for visually impaired individuals who heavily rely on it for accessing information. However, a significant portion of text materials on the internet remains inaccessible to blind users, limiting their opportunities for development and inclusion in various communities worldwide. Leveraging audio virtual environments, predominantly through screen reader technology, offers a notable advantage for blind individuals in accessing online applications like emails. This study presents a new design aimed at facilitating the email retrieval for blind users with the utilization of speech-to-text and text-to-speech technologies. This approach is particularly suitable for individuals with average human capabilities and a limited level of education, as it relies solely on interactive voice response for input, ensuring effective functionality for the proposed system. © 2024 IEEE.

关键词： Application programming interfaces (API)

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Fake/Counterfeit Medical Product Identification Using Blockchain & QR Code 2

Fake/Counterfeit Medical Product Identification Using Blockc...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Devanshu Singhal, Aryan Singh, Khushi Singh, Pushpa Gl Bajaj Institute of Technology and Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Counterfeiting in the field of medicine has raised substantial concerns as a global issue, with its prevalence noticeably expanding in recent years. Counterfeit medicines pose a particularly severe threat to patient's life or safety and cause severe implications. It is difficult to trace a medical product as well as to identify its authenticity. This paper aims to combat the widespread issue of counterfeit goods in industries like Pharmaceuticals and Healthcare by creating a Blockchain based product identification system. Combining the potential of Blockchain technology along with a secure QR code mechanism will offer robust security, immutability of data and have transparency in all the transactions. Thus, this system will ensure product traceability in the supply chain whereas the QR code will make it feasible for consumers to access product information instantly. © 2024 IEEE.

关键词： blockchain counterfeiting identification medicines QR code

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Wearable Sensor-Based Gait Analysis: Advanced Feature Extraction Techniques 2

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Karthik Salian, Preethi K. Ranjith, H.D. Salian, Supriya Jayashree Shetty, K. Annapoorneshwari NMAM Institute of Technology Udupi Nitte India NMAM Institute of Technology Udupi Nitte India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Mangalore India

ISBN: (纸本)9798350383867

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-dept. analysis, enabling effective feature extraction for comprehensive gait assessment. © 2024 IEEE.

关键词： Accelerometers

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Revolutionizing Traditional Supermarkets: A Comprehensive Solution for Enhancing Efficiency, Security and Customer Satisfaction 2024

Revolutionizing Traditional Supermarkets: A Comprehensive So...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Tanna, Om H. Vasani, Ankur M. Thakkar, Krishna H. Gotecha, Malav J. Dept. of Information Technology MPSTME NMIMS University India Dept. of Information Technology Shri Bhagubhai Mafatlal Polytechnic India Dept. of Computer Engineering DJ Sanghvi College of Engineering India

ISBN: (纸本)9798400709722

This research proposes an Artificially Intelligent enabled Smart Supermarket System for enhanced efficiency, security and reduces man power as compared to traditional Supermarkets. In order to ensure a perfect omnichannel shopping experience the system uniquely includes an Artificially Intelligent model. Customers can scan items on the go with the app in their smartphones, select product variants as required, add the product to their virtual cart and pay online for the total cart value. With the help of YOLOv5 computer vision algorithm, eliminating long checkout queues and manpower the system proves to be revolutionizing the traditional supermarket procedures. Moreover, the system also incorporates the indoor navigation assistance on customer's app to facilitate easy access and navigation to preferred items. This futuristic solution not only provides valuable insights for business development but also is proven to be more efficient and helpful for customers saving their time and companies resources. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Advancing Medical Imaging: A Comprehensive Synthetic Dataset for Infant Brain MRI Analysis 2

Advancing Medical Imaging: A Comprehensive Synthetic Dataset...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Yashasvi, B.N. Kavya, N.P. R N S Institute of Technology VTU Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350371314

Accurate diagnosis and treatment planning are consistently challenging tasks in the medical field, especially when reliant on image diagnosis. The availability of sufficient and high-quality datasets becomes crucial in addressing this challenge. To tackle this issue, synthetic datasets can play a vital role in augmenting the number of samples while maintaining good quality. This study focuses on utilizing Deep Convolutional GANs (DCGANs) and Wasserstein GANs (WGANs) to generate synthetic datasets, specifically for Infant Brain MRI imaging. The Human Connectome Project (dHCP) dataset, comprising 800 samples categorized into four classes, was used for this study. Several key metrics, including the Similarity Index (SSI), Inception Score (IS), and Fréchet Inception Distance (FID), were employed to evaluate the performance of the generated synthetic datasets. The Inception Score was calculated for each class individually as well as for the overall synthetic dataset. For the normal class (first class), DCGAN achieved an Inception Score of lS.45, while WGAN scored 15.13. In the second class, DCGAN scored 19.70 compared to WGAN's 16.21. Similarly, in the third class, DCGAN scored lS.65 and WGAN 15.65, and in the abnormal class (fourth class), DCGAN scored 17.58 against WGAN's 16.00. The overall synthetic dataset scored 19.29 for DCGAN and 16.47 for WGAN. As the higher Inception Scores across different classes and for the overall dataset denoted that DCGAN performed well compare to WGAN in generating synthetic images for the Infant Brain MRI dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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