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Fall Detection Using Sound with FPGA Signal Analysis and Machine Learning integration

Fall Detection Using Sound with FPGA Signal Analysis and Mac...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Andre, Mariano Viegas Ozdemir, Ahmet Turan Kavuncuoglu, Erhan Erciyes University Department of Electric and Electronic Engineering Kayseri Turkey Erciyes University Department of Electric and Electronic Engineering KayseriTR-38039 Turkey Gemerek Vocational School Cumhuriyet University Department of Computer Tecnology SivasTR-58840 Turkey

ISBN: (纸本)9798331505745

Falls represent a considerable risk to the well-being of geriatric populations, frequently resulting in injuries or mortality. In response to this pressing concern, we introduce an innovative fall detection apparatus employing acoustic sensors and FPGA (Field Programmable Gate Array) technology to effectively alleviate these hazards. Our system capitalizes on acoustic phenomena, particularly those associated with footstep sounds and falls, which are gathered through routine activities and video documentation to develop a comprehensive model adept at differentiating between fall incidents and non-fall occurrences. The primary attributes encompass the derivation of Mel-frequency cepstral coefficients (MFCC) from the processed auditory signals. To enhance operational efficacy, an FPGA is utilized to execute all data processing functions, inclusive of Fourier transformations and a threshold-based predictive algorithm. This methodology diminishes dependence on conventional computational systems while preserving elevated processing efficiency. The derived features are subsequently employed to formulate a data classification model utilizing a Convolutional Neural Network (CNN) and Random Forest Tree (RFT), which proficiently discriminates between sounds indicative of falls and those not related to falls. © 2025 IEEE.

关键词： Data integration

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Reconfigurable Smart Grid Management for Electric Vehicle Charging and the integration of Renewable Energy Sources using Pied Kingfisher Optimizer 2

Reconfigurable Smart Grid Management for Electric Vehicle Ch...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Sivaramkrishnan, M. Ramkumar, M. Siva Uday, Kaparthi Kanan, Mohammad Raj, C. Mohan Giri, Jayant Karpagam College of Engnieering Dept of EEE India SNS College of Technology Dept of ECE India CVR College of Engineering Ibrahimpatanam EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

Electric Vehicle (EV) charging management under the Smart Grid (SG) framework with incorporated Renewable Energy Sources (RESs) functions to advance Energy Management (EM). The operational cost of EV charging varies because renewable energy availability can change so this affects the financial performance of charging operations. integration of RESs with the SG becomes challenging for operational system efficiency particularly when renewable energy generation levels are low. To overcome these drawbacks, this manuscript proposes an efficient approach for SG management for EV charging with the integration of RESs. The suggested method is Pied Kingfisher Optimizer (PKO). The main aim of the suggested technique is to minimize the operational expenses of EV charging as well as maximize the efficiency of the SG system. The PKO is used to optimize the scheduling and allocation of energy resources within the SG system. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are currently in use. The suggested technique outperforms all existing approaches such as Bald Eagle Search (BES), Red Kite Optimization (RKO), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Coati Optimization Algorithm-Constitutive Artificial Neural Network (COA-CANN). The suggested approach delivers superior performance than conventional methods by reducing operational costs to 638,322$ under coordinated charging and 629,745$ under smart charging without impacting system performance metrics. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring 2

Wearable Biosensors: Transformative Innovations and Future P...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Thute, Trupti Gudadhe, Amit Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Maharashtra Wardha India

ISBN: (纸本)9798331505745

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare. © 2025 IEEE.

关键词： mHealth

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Smart Sanitation Management: Integrating IoT, AI-Driven Analytics, and Automated Sensing for Efficient Public Toilet Maintenance 2

Smart Sanitation Management: Integrating IoT, AI-Driven Anal...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Haripriya, K. Preethi, C. Hammed, S.Shahul Pavalarajan, S. Sharon, S. Akhil Suryakumar, S. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

An IoT-based self-sustained public toilet maintenance system aims to improve hygiene, efficiency, and resource management. It integrates smart sensors, microcontrollers, and cloud connectivity to monitor parameters such as water levels, air quality, and toilet usage in real-time. AI-driven predictive maintenance optimizes cleaning schedules, minimizes downtime, and ensures efficient resource use. Automated water control prevents wastage, while air quality sensors detect harmful gases and activate ventilation. A smart notification system alerts municipal authorities about supply shortages, enabling timely replenishment of water, soap, and tissues. By reducing manual intervention, the system lowers operational costs and improves service quality. Cloud connectivity allows seamless data processing, offering insights for better sanitation management. The integration of IoT and AI ensures proactive maintenance responses. A comparative analysis highlights the novelty of this system in automating and optimizing maintenance. © 2025 IEEE.

关键词： Replenishment (water resources)

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Investigation of Topological Behavior and Glass Transition Temperature of Chalcogenide M5-Se60-Te20-Ge15 (M = Sn, Zn, Sb) Glassy Alloys 2nd

Investigation of Topological Behavior and Glass Transition T...

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2nd International conference on Mechanical Engineering, INCOM 2024

作者： Patra, Debtanu Biswas, Dipankar Mondal, Rittwick Ghosh, Bidyut Kumar Modak, Nipu Department of Mechanical Engineering Jadavpur University Kolkata700032 India Department of Mechanical Engineering Regent Education and Research Foundation Barrackpore Kolkata700121 India Department of Electronics and Communication Engineering Institute of Engineering and Technology GLA University Uttar Pradesh Mathura281406 India Department of Science Chowhatta High School West Bengal Birbhum731201 India Department of Electrical Engineering Regent Education and Research Foundation Barrackpore Kolkata700121 India

ISBN: (纸本)9789819766666

Throughout this communication, it has been reported the theoretical outcomes of doping of various additive elements on the glass composition Se60-Te20-Ge15-M5 (M = Sn, Zn, Sb). The dependence on mean coordination number, mean bond energy, the transition temperature of the glass, and cohesive energy with various additive elements have been studied based on topological considerations. A comprehensive investigation of various parameters like mean coordination number, bond-stretching and bond-bending parameters, energy gap, cohesive energy, glass transition temperature, and mean bond energy has been carried out. By deploying basic ideas grounded in average coordination numbers and bond energies, the cohesive energies of the systems have been estimated by computing the number of each type of predicted bond. Band gap energies of the systems have been estimated theoretically and the Sb-doped system possesses the lowest energy band gap with a value of 1.39 eV. The chemically ordered network model and constraints theory have been used to discuss these obtained results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Glass transition

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An IoT Based Access Control system Using R307 Optical Biometric Sensor 2

An IoT Based Access Control System Using R307 Optical Biomet...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Anusha, R. Shankari, N. Kotemane, Sukhesh Kumar Shetty, Surabhi S. Uppoor, Srijan S. Shetty, Yashwin K. NMAM Institute of Technology India

ISBN: (纸本)9798331505745

This work offers the design of an IoT-based access control system using the R307 optical biometric sensor that increases the use of fingerprints for secure access. This system is developed to provide a safe and effective means of securing the restricted areas so adaptable for the different places such as offices, homes, and industries. The hubs of the system are the R307 biometric sensor that scans and verifies the user's fingerprints to unlock the access. The fingerprints are then recorded in the system, only the security personnel they allow access to the facility. This biometric form of identification not only enhances security by rendering door lock and unlocking impossible without the human feature being scanned but also does away with conventional security devices such as keys and cards which are always easy to misplace, copy or even have stolen. The system boasts integration with the IoT, hence its strength due to features such as remote control. Added to that is the provision for attempt log, and in most cases, real-time notifications;thus, the system is portable and expandable. This was especially true for the large-scale scenarios where access points are concurrently utilized. Besides offering highly secure access control models that integrate the fingerprint technology, the system comes with an easily understandable module for enrolling the fingerprints besides other administrative options that allow easy management of the system by both the system administrators and the ordinary users. Apart from this, the IoT framework leaves scope for further development. It is possible to integrate with other security systems or attach new sensors in order to raise the factors for authentication. © 2025 IEEE.

关键词： Authentication

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IoT-Enabled Smart Classrooms for Enhanced Learning Environments

IoT-Enabled Smart Classrooms for Enhanced Learning Environme...

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Computational Intelligence, Communication technology and Networking (CICTN), International conference on

作者： Prem Kumar Priyanshu Negi Priyanka Sharma Pratham Mittal School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Innovative integration of digital infrastructure with pedagogical practices in rapidly transforming educational environments. The technology is well recognized yet fails to solve issues in traditional classrooms due to poor resource management, less individualized learning experiences, and real-time performance monitoring. The research focuses on these issues and answers them by designing and implementing an IoT-enabled smart classroom ecosystem aiming at changing the learning environment. Developed an end-to-end IoT framework that incorporates sensor networks, machine learning algorithms, and adaptive learning interfaces across multiple educational institutions using a mixed-method approach. This included prototype development, systematic data collection, and iterative performance analysis using advanced analytics and user experience assessments. Significant learning engagement improvements by 23.7%. Resource usage efficiency improves by 35.2%. Excellent Personalized learning results, Real-time environmental monitoring of the IoT system, Adaptive instructional strategy with the complete track record of the student. The precision control in climate through sensors for an optimum lighting effect Smart management of the space using sensors. In sum, our research establishes a transformative model for technology-enhanced educational spaces, providing a scalable blueprint for future smart learning environments where technological innovation is integrated into pedagogical effectiveness.

关键词： Temperature measurement Technological innovation systematics Space technology Prototypes Educational technology Aerospace electronics Real-time systems User experience Resource management

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Innovative Application of Computer technology in Agricultural and Educational Education 24

Innovative Application of Computer Technology in Agricultura...

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Proceedings of the 2024 2nd International conference on Information Education and Artificial Intelligence

作者： Yangfan Liu School of Foreign Languages Jilin Agricultural Science and Technology University Jilin Jilin China

ISBN: (纸本)9798400711732

Under the background of informatization, computer technology has gone deep into various fields, including farming and reading education. Based on the practice of agricultural curriculum reform, this paper focuses on three innovative paths: constructing situational teaching mode to break the time and space constraints; Develop intelligent learning guidance system to achieve precise education; Build a cloud based interactive platform to promote dialogue among farming civilizations. Through the analysis of typical cases, this paper reveals how the integrated application of digital technologies such as virtual simulation, learning portrait and cross domain collaboration can significantly improve the teaching efficiency, and provide an operable solution for the integration of agricultural culture inheritance and modern agricultural education.

关键词： Application of cross cultural communication technology in education digitalization

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Warehouse Management and Tracking system for University Assets Using IoT technology to Optimize Operations and Reduce Randomness

Warehouse Management and Tracking System for University Asse...

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Advanced Innovations in Smart Cities (ICAISC), International conference on

作者： Salma Elhag Alaa Alrfeai Bashair Aljoudi Ekram Alhazmi Department of Information System King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Efficient asset management within university environments is a crucial aspect of ensuring smooth daily operations. Universities often struggle with disorganized tracking systems for their tools, equipment, and furniture, leading to losses or delays in operations. This project aims to design a robust warehouse management and tracking system using Internet of Things (IoT) technology to optimize the management of university assets. The proposed system will allow real-time tracking, monitoring, and reporting of assets to prevent random losses and streamline the process of checking out and returning equipment. The integration of IoT devices will facilitate seamless data collection, reducing manual errors, ensuring accountability, and enhancing overall efficiency. This solution will address the core issue of disorganization in university storage and asset management, providing a sustainable and scalable system for the future.

关键词： Technological innovation Smart cities Manuals Data collection Real-time systems Asset management Delays Internet of Things Optimization Monitoring

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RhinoGuardNet: An Integrated Deep Learning system for Monitoring, Movement Prediction, and Threat Detection in Javan Rhino Conservation 2

RhinoGuardNet: An Integrated Deep Learning System for Monito...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Darney, P. Ebby Fernando, A. Vegi Krishnan, R. Santhana Raj, J. Relin Francis Lavanya, V. Kumar, V. Vinoth Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India Bangalore India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331505745

The Javan rhinoceros (Rhinoceros sondaicus), one of the most critically endangered species, faces numerous threats such as poaching, habitat destruction, and limited monitoring capabilities. Traditional conservation methods are often ineffective due to the vast and difficult-to-access nature of rhino habitats. This paper proposes a Hybrid Deep Learning (HDL) framework, combining CNNs, RNNs, and GNNs, to address these challenges and enhance conservation efforts. The proposed system offers a comprehensive solution for real-time rhino monitoring, poacher detection, and habitat preservation. The framework integrates CNNs for image-based monitoring, enabling the detection of rhinos, poachers, and habitat changes using camera trap images, drone footage, and satellite images. RNNs, specifically LSTM networks, are employed to analyze bioacoustic recordings from forest microphones, capturing long-term dependencies in sound data to detect rhino vocalizations and poaching sounds like gunshots or chainsaws. In addition, GNNs are utilized to process GPS tracking data to model spatial-temporal relationships and predict rhino movement, optimizing anti-poaching patrol routes and identifying high-risk zones for poaching. The proposed framework has been assessed using various evaluation metrics such as mAP for object detection, F1-Score for audio classification, and RMSE for movement prediction. The system is designed to be deployed in the field on a cloud-based platform, enabling real-time monitoring and decision-making. Edge computing on drones and sensor devices ensures low-latency processing and immediate action in response to detected threats. This model is expected to significantly enhance Javan rhino conservation by providing accurate, real-time data and actionable insights. © 2025 IEEE.

关键词： Deep learning

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