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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Puthucode Ganesh Vignesh Nithin Kumar S Senthil Kumar T Jeyakumar G Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments.

关键词： Privacy Accuracy Laser radar Three-dimensional displays Sensors Indoor environment Convolutional neural networks Fall detection Older adults Monitoring

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Detection of Sybil Attacks in Vanet using Transformer With CNN Hybrid Architecture and Optimized by AdamW Optimizer

Detection of Sybil Attacks in Vanet using Transformer With C...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Abishek M Benny Israel J Adlin Sheeba Sundarababu Maddu Dept. of Computer Science and Engineering St.Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Vehicular Ad Hoc Networks (VANETs) help improve road safety by letting cars talk to each other but they still face Sybil attacks. In a Sybil attack, bad nodes claim many identities and weaken the network. This paper offers a Transformer-CNN model that uses the AdamW optimizer to spot Sybil attacks in VANETs. It uses spatial as well as time data from vehicular communications. The model works best at finding these attacks. The system is measured with metrics like accuracy, precision, and it shows big gains compared to older methods.

关键词： Adaptation models Accuracy Image edge detection Vehicular ad hoc networks Gain measurement Transformers Feature extraction Road safety Real-time systems Overfitting

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing

Movie Genre Prediction Using AI-Generated Data and Natural L...

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International Conference on computer and Information Technology (ICCIT)

作者： Sifat Tanvir Ayesha Parpia Abontee Md. Abu Rayhan Md. Fuadur Rahman Tasnim Sultana Ajwa Ahmed Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer Science Brac University Dhaka Bangladesh

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved.

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3D-CAN: A 3D Convolution Attention Network for Feature Extraction and Classification of Hyperspectral Images

3D-CAN: A 3D Convolution Attention Network for Feature Extra...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Murali Kanthi K. Venkateshwara Rao L. Chandra Sekhar Reddy T. Hitendra Sarma Nuthanakanti Bhaskar Nam Vasundhara Dept. of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad Telangana India Dept. of Computer Science & Engineering (Data Science) CMR College of Engineering & Technology Engineering Hyderabad Telangana Dept. of Information Technology Vasavi College of Engineering Hyderabad Telangana India Dept. of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana India

In recent years, the deep learning method has gained widespread popularity in hyperspectral image classification (HSI) tasks because of remarkable feature extraction capability. However, conventional deep learning approaches often yield suboptimal classification performance due to the limited discrimination ability of the extracted features. Moreover, the challenge of obtaining high classification performance with a limited number of samples in hyperspectral images (HSIs) has emerged as a prominent research area of interest. To address this concern, this letter provides a novel 3D convolution attention network (3D-CAN) for feature extraction and HSIs classification. The attention mechanism employed in this context enables capturing both the long-range spatial dependencies of HSIs in the horizontal and vertical directions and the varying significance levels across distinct spectral channels. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy.

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Forensics Investigation on Social Media Apps and Web Apps Messaging in Android Smartphone

Forensics Investigation on Social Media Apps and Web Apps Me...

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2023 International Conference on Smart applications, Communications and Networking, SmartNets 2023

作者： Abu Hweidi, Ruwa F. Jazzar, Mahmmoud Eleyan, Amna Bejaoui, Tarek College of It Palestine Technical University - Kadoorie Dept. of Computer Science Tulkarem Palestine Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798350302523

The widespread usage of smartphones in many domains such as education, finance, organizations, and gaming makes hackers covet them and raises the challenge of facing cybercrime and forensic investigation of these devices. The devices run apps that can be sophisticatedly linked to other devices or platforms, or Web Apps, which duplicate the forensic investigation problem. Each social media application has its feature to deal with various types of messages and has its method of receiving and storing the messages depending on their type, i.e., text, video, contact file, image, and record. This study works on two scenarios to track the recovered data by logical acquisition method in MOBILedit and FINALmobile forensic tools. The first scenario is social media massaging between two smartphone Apps, and the second one is between Web Apps and smartphone Apps. In the two scenarios, the acquired device is the receiver's smartphone. This study finds that logical acquisition cannot recover any Facebook or Instagram data, but in WhatsApp and Telegram, logical acquisition can extract and analyze all data except text and contact files, without knowing which linked device or Web App sent the message. There is a need for more investigation on social media Apps and Web Apps in future works. © 2023 IEEE.

关键词： Smartphones

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Facial Biometrics-Driven Feature Encoding and Deep Learning Integration for Secure Password Retrieval

Facial Biometrics-Driven Feature Encoding and Deep Learning ...

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Disruptive Technologies (ICDT), International Conference on

作者： Akash Immanuel Solomon A Ashwin M Swaminathan K Dept. of Computer Science St. Joseph's College of Engineering Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The proposed system includes face recognition-based systems designed for secure identity verification and decentralized data storage. The working is supposed to be done in three phases: data capture, training of the machine learning model, and decentralized data storage. Collection in the first phase will include face images of individuals and government-issued identification details, such as Aadhar and PAN. A machine learning model will be trained in the second phase for improving the accuracy in the face recognition process. The third phase ensures secure storage of the recognized faces and associated personal data through a decentralized approach similar to blockchain technology. Security, privacy, and resilience against tampering are further developed in this design. The proposed system will reliably meet the application requirements that involve strong authentication processes and protection of data, therefore making the sectors using these systems include government services and secured personal data management.

关键词： Training Privacy Face recognition Government Memory Passwords Reliability Protection Secure storage Resilience

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Predictive Insights Beyond Boundaries: Integrating Clinical Data for Hepatitis C Forecasting Using Machine Learning and Deep Learning

Predictive Insights Beyond Boundaries: Integrating Clinical ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Fatema Tuj Jenin Al-Amain Khandaker Mohammad Mohi Uddin Faizul Ahmed Robin Md. Ferdous Bin Hafiz Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A person's well-being and wellness are seriously hampered by the chronic diseases' lasting implications and recurrent signs. A startling 58 million people worldwide suffer from an incurable chronic form of hepatitis C, disease is brought about by a virus called hepatitis C (HCV). Additionally, it results in 290,000 deaths every year from cirrhosis and liver cancer, both of which linked to HCV. The goal of this venture is to reliably identify and anticipate hepatitis C utilizing Machine Learning (ML) and deep Learning (DL) techniques. It does this by utilizing ML and DL algorithms' capacity to scan big datasets and spot patterns and associations that help with diagnosis. Predictive models were created utilizing blood values from 615 patients, including both hepatitis patients and healthy blood donors, using a variety of approaches comprising SVM, Logistic regression (LR), KNN, LSTM and others. The dataset underwent extensive preprocessing, which included managing missing values, scaling, and diminished dimensionality using Principal Component Analysis (PCA). After a variety of ML along with the SVM classifier showed the best result, giving an accuracy concerning 98.33%, recall concerning 94.17%, precision concerning 93.99%, and F1-score concerning 94.16%.

关键词： Support vector machines Deep learning Accuracy Liver diseases Predictive models Reliability Long short term memory Viruses (medical) Medical diagnostic imaging Principal component analysis

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FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays

FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneu...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Ayush Roy Anurag Bhattacharjee Diego Oliva Oscar Ramos-Soto Francisco J. Alvarez-Padilla Ram Sarkar Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Universidad de Guadalajara CUCEI Guadalajara Mexico Dept. of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://***/AyushRoy2001/FA-Net

关键词： Solid modeling Pneumonia Fluids Computational modeling Lung Feature extraction Task analysis

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Weakly Supervised Road Mark Detection for Advanced Driver Assistance Systems in Consumer Automotive Technology

Weakly Supervised Road Mark Detection for Advanced Driver As...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Xiu-Zhi Chen Xiao-Wei Huang Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Road mark detection is a critical component of Advanced Driver Assistance Systems (ADAS), ensuring accurate lane-keeping, navigation, and driver safety. However, the high cost of acquiring fully annotated datasets for supervised learning poses a significant challenge for real-world applications. This paper proposes a novel weakly supervised learning method for road mark detection tailored to consumer automotive technology. Leveraging weak annotations, the method efficiently learns to detect and classify road marks with minimal human intervention. The approach was evaluated on the publicly available CeyMo dataset, achieving a classification accuracy of 99.3% and a localization accuracy of 71.5%, demonstrating its effectiveness and practicality. The proposed method reduces the dependency on fully annotated data while maintaining competitive performance, making it a scalable solution for consumer-level ADAS applications. This work contributes to advancing cost-effective, reliable, and scalable ADAS technologies, paving the way for broader adoption in intelligent vehicles.

关键词： Location awareness Accuracy Weak supervision Intelligent vehicles Roads Supervised learning Safety Reliability Advanced driver assistance systems Automotive engineering

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Balancing Real-world Interaction and VR Immersion with AI Vision Robotic Arm 20

Balancing Real-world Interaction and VR Immersion with AI Vi...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Xiaoran Zhan, Yang Iwasaki, Yukiko Shi, Miaohui Tang, Shijie Iwata, Hiroyasu Waseda University Dept. Modern Mechanical Engineering 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan Waseda University Dept. Computer Science and Engineering 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan

ISBN: (纸本)9798350320831

With the widespread adoption of Virtual Reality (VR) technology, VR devices can be more realistic and make the tasks they perform feel real to the user. It is this sense of realism that makes more and more people are using VR headsets. While using VR devices, the need to interact with the real world will also exist. For example, when people are using a VR headset to study and work, they have a physiological need (thirst, hunger, etc.), and need to interact with reality. But the closed nature of the VR headset causes this interaction to be very disruptive to the immersion of the user's current task. To address this issue, we propose an interaction method that combines an AI vision algorithm and a robotic arm to preserve immersion during task performance in VR. This method addresses the challenge of interacting with the real world while using closed VR devices. Our experiments show that our method outperforms see-through and mapping methods, allowing users to quickly re-immerse themselves after interruptions and reducing interruptions during real-world task performance. Future research will focus on improving this method without sensitization and expanding the range of real-world operations that can be performed. © 2023 IEEE.

关键词： Virtual reality

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