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Enhancing real-time IoT applications in Cloud Communication Networks Through Adaptive Intelligent Routing and Secure Data Transmission 2

Enhancing Real-Time IoT Applications in Cloud Communication ...

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

作者： Swathi, B. Ramesh, B. Latha, Kambala Madhu Bhavsingh, M. Padma, M. Manjunatha, N. G Pulla Reddy Engineering College Department of CSE Andhra Pradesh Kurnool India CVR College of Engineering Department of ECE Hyderabad India Avanthi's Research and Technological academy Andhra Pradesh India Ashoka Women's Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Lecturer in Computer Applications Government Degree College Srisailam Project Andhra Pradesh Nandyala India Shri Jagdishprasad Jhabarmal Tibrewala University Department of CSE Rajasthan India

ISBN: (纸本)9798331505745

The growing Internet of Things network with cloud technology enables real-time solutions in healthcare, autonomous systems, and industrial automation. These systems face challenges with response time, shifting network layouts, power conservation, and data security threats. RPL and traditional routing protocols struggle to adapt to complex networks, while machine learning-based and EERP approaches solve some issues but leave others unresolved. This study presents an AI routing method that enhances live data transfer between IoT systems and cloud platforms. The algorithm tracks network shifts to make fast routing decisions, reducing time delays and conserving power. This study implement basic encryption, device confirmation, and abnormal event recognition to protect data transfers. Simulations compared the technique's performance to RPL and EERP approaches, measuring latency, data delivery rate, energy usage, network speed, and security capabilities. Testing shows a 25-50% improvement in latency and 10-20% better PDR results. Energy utilization decreases by 30-50%, while throughput remains superior. Security features demonstrate reduced encryption overhead with improved anomaly detection and authentication. This research addresses IoT-cloud communication network challenges through an adaptable system that conserves power, protects data, and enhances reliability for time-sensitive applications. © 2025 ieee.

关键词： Internet of things

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Hands-On Gaming: real-time Gesture Detection in Rock-Paper-Scissors Using Mediapipe and CNN

Hands-On Gaming: Real-Time Gesture Detection in Rock-Paper-S...

引用

2025 international conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Darmawan, Irfan Meldiawati, Aulia Melda Rizal, Randi Rahmatulloh, Alam Haerani, Erna Gunawan, Rohmat Telkom University Department of Information Systems Faculty of Industrial Engineering Bandung Indonesia Siliwangi University Faculty of Engineering Departement of Informatics Tasikmalaya Indonesia Siliwangi University Faculty of Engineering Department of Information Systems Tasikmalaya Indonesia

ISBN: (纸本)9798331513320

real-time hand gesture detection enables intuitive non-verbal communication between humans and devices. In gaming, using hand gestures as a control mechanism enhances user engagement and provides a more immersive experience. This research implements a real-time hand gesture detection system for the Rock-Paper-Scissors game by combining Mediapipe for feature extraction and a Convolutional Neural Network (CNN) for gesture classification. Mediapipe detects and extracts key hand landmarks in real-time, while the CNN classifies the gestures into three categories: rock, paper, and scissors. The proposed system demonstrates high performance, achieving an impressive classification accuracy of 98.12% after 500 training epochs. Furthermore, it ensures adequate detection speed for seamless real-time interaction in gameplay. Future improvements to this system include utilizing a larger and more diverse dataset to enhance the model's ability to recognize a broader range of hand gesture patterns. This approach could improve robustness across different lighting conditions, hand sizes, and variations in gesture execution. Additionally, refining the CNN model architecture could lead to further performance gains in both accuracy and speed. The findings of this research highlight the potential for hand gesture detection to enhance interactivity in gaming and beyond. applications such as augmented reality (AR), virtual reality (VR), and other interactive systems could benefit from this technology. This study demonstrates that integrating Mediapipe and CNNs can provide an accurate, responsive, and engaging user experience, paving the way for further advancements in gesture-based interaction technologies. © 2025 ieee.

关键词： Convolutional neural networks

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Inferencing CNN Model for Navigational Object and Obstacles Classification on STM32 Boards 7th

Inferencing CNN Model for Navigational Object and Obstacle...

引用

7th international conference on Communication, Device and Networking, ICCDN 2024

作者： Mawblei, Mainkordor Peesapati, Rangababu Binong, Juwesh North Eastern-Hill University Shillong793022 India Indian Institute of Information Technology Design and Manufacturing Kurnool518008 India

ISBN: (纸本)9789819764648

This report explores the integration of high-end neural network models into embedded systems, its advantages over the high-end computers and Cloud-based inferencing to overcome the drawbacks such as cost, power consumption, latency, security concerns, and reliability. The STM32 microcontrollers are widely used in various embedded applications and are known for their high performance, low power consumption, and extensive connectivity options. A CNN architecture, ConvNet3, is proposed and fine-tuned for object classification and navigating obstacles in real-time scenarios, leveraging the limited resources of the STM32F401RE board. The performance of these models is evaluated, considering factors like prediction speed, accuracy, and model size. The findings highlight the trade-offs between accuracy and size reduction achieved through compression techniques. Overall, this research contributes to advancing CNN-based solutions for embedded systems and offers insights for model selection and data preparation, facilitating improved navigational capabilities and object recognition in resource-constrained environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： embedded systems

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IMC Based PID Controller for Spherical Tank Liquid Level Control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st international conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 ieee.

关键词： Three term control systems

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Deep Learning Based Export Oriented Guppy Fish Health Classification Method 5

Deep Learning Based Export Oriented Guppy Fish Health Classi...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Gunathilaka, K.I.C. Prabhath Buddhika, R.A. Nsbm Green University Department of Electrical Electronic and Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331530983

Sri Lanka is one of the largest exporters of ornamental fish in the world. Guppies as a species are the largest portion of the total export volume. Meeting the health standards of the export destination is paramount and that matters a lot for the reputation of the country as a leading exporter. This research is concerned with the use of deep learning in the classification of healthy and unhealthy guppies. Specifically, transfer learning (TL) was utilized to retrain the YOLO model for this task. The study incorporated modern deep learning technologies in creating a model that will be effective in differentiating normal fish from the diseased. The created model was used for the test, and it yielded an accuracy rate of 90%, which proves the model's efficiency for real-world applications. Moreover, the time taken in the identification of the diseased fish was greatly shortened by the automated method as compared to the manual approach, and that provides a practical solution for the industry. Besides improving the quality of the health assessment, integration of deep learning into the assessment of ornamental fish will also help to modernize the overall ornamental fish export industry. © 2025 ieee.

关键词： Computer vision

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Efficient Resource Management for real-time AI systems in the Cloud using Reinforcement Learning 3

Efficient Resource Management for Real-Time AI Systems in th...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Vedula, Jagadeesh Baskaran, Saravanakumar Sayiram, Sathyanarayanan Muthusamy, Rajkumar Gupta, Ketan Yuvaraj, N. Digital Transformation HoustonTX United States Microsoft Corporation SeattleWA United States Ibm San JoseCA United States Ntt Data IrvingTX United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331508456

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The research utilizes reinforcement learning to enable dynamic resource allocation, ensuring that resources are provisioned on demand rather than relying on static allocation methods defined at design time. © 2025 ieee.

关键词： Reinforcement learning

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Weed Detection in Farmlands using RCNN 3

Weed Detection in Farmlands using RCNN

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sivakumar, V. Lokit Sushruth, T. Jayaa Suthersen, A.R. Gugan, S.K. Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Detection and classification of weeds are important for the optimization of farming applications as it allows to identify species so that they can be removed, competing with others who need nutrients to grow their crop. A variety of methods have been used in different research studies but there still exists a research gap for a comprehensive comparative analysis of multiple machine learning models. Here, we attempt to provide a comparative study for classic/not ensemble (Support Vector Machines (SVM), Random Forest etc.) machine learning models with Region-Based CNNs (RCNN). Last, we stack LSTM (a type of RNN) onto the model to making it more powerful with respect to time learning and performance.1300 images of sesame crops and different types of weeds, serving as a proxy for diverse agricultural scenarios. The proposed weed detection and classification technique demonstrates strong potential for practical applications, particularly when integrated with autonomous robots for real-time field deployment. The study contributes to the field of precision agriculture by providing comparative insights into machine learning and deep learning models, emphasizing the importance of temporal learning for weed management. © 2025 ieee.

关键词： Support vector machines Precision agriculture Crops Nutrients real-time systems Internet of Things Long short term memory Random forests Optimization Farming

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Container Performance in Space systems 25th

Container Performance in Space Systems

引用

25th international conference on Internet computing and IoT, ICOMP 2024, and 22nd international conference on embedded systems, Cyber-physical systems, and applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

作者： Alamgir, Shariar M. Bohra, Dhruv L. Nguyen, Elisabeth A. Embedded and Specialized Computing Department The Aerospace Corporation El SegundoCA United States Software Architecture and Engineering Department The Aerospace Corporation El SegundoCA United States

ISBN: (纸本)9783031859229

Critical embedded software, such as spacecraft flight software, requires high reliability, efficiency, and real-time performance while being capable enough to meet application needs. The increasing prevalence of Linux, and in particular Linux containers, presents opportunities for space systems but also poses potential risks since Linux was not designed to be a real-time operating system. In this study, we conducted a variety of experiments to look at both the overhead imposed by containers and the latency experienced in Linux using various combinations of patched OS and containers running on a Raspberry Pi 4. We found that containers have little impact on CPU utilization but do incur penalties in RAM, disk storage, and startup time. We also found the unpatched Linux kernel performs well for real time;and that, while the real-time kernel patch reduces both overall and maximum latency, it can negatively impact performance, particularly on single-core systems running non-real-time tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linux

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Detection of Malicious Interpreted Code using ML to Optimize Parameters for Minimizing Prediction Errors

Detection of Malicious Interpreted Code using ML to Optimize...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dhawan, Manoj Kuril, Devendra Panadiwal, Himanshu Rawat, Hitesh Rawat, Anjali Rawat, Romil School of Engineering Avantika University Ujjain India Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Information Technology Indore India University of Extremadura Department of Business Management and Economics Badajoz Spain University of Extremadura Department of Computer and Communication Technology Badajoz Spain LabGeoInf-Research LABoratory in GEOmatics and INFormation systems Rome Italy

ISBN: (纸本)9798331538538

Mobile devices face growing cybersecurity threats, including malware, data breaches, malicious applications, and social engineering attacks. Cybercriminals exploit these vulnerabilities to gain unauthorized access, disrupt communication, and compromise sensitive information. Conventional malware detection methods, such as signature-based and heuristic techniques, often struggle to identify evolving threats due to their reliance on predefined patterns. This study presents an advanced malware detection framework that integrates a Reservoir Network with a Deep Learning Approach (DLA) to enhance detection accuracy and minimize prediction errors. The proposed method utilizes two static features-API call permissions and alert signals- to improve detection efficiency. Experimental evaluation, conducted using the Drebin malware dataset, demonstrates that the proposed model achieves an accuracy of 99.7 %, reduces the false positive rate to below 1 %, and ensures optimal processing speed with a batch size of 64 and a learning rate of 0.001. Compared to traditional approaches, this framework exhibits superior adaptability to obfuscated malware while enabling real-time threat detection. The findings emphasize the effectiveness of machine learning (ML)-driven security mechanisms in addressing emerging cybersecurity threats. © 2025 ieee.

关键词： Computer viruses

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Optimizing Multimodal Transformers for Medical Image Captioning: Enhancing Automated Descriptions via AI systems 6

Optimizing Multimodal Transformers for Medical Image Caption...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Arman, Mithila Jahan, Md. Khurshid Dhrubo, Ahmed Faizul Haque Rhaman, Md. Mahfuzur Choya, Sumaya Binte Zilani Dohan, Din Mohammad Ul Islam Sajid, Md. Ashiq Rabiul Alam, Md. Golam BRAC University Dept. of CSE Dhaka Bangladesh North South University Dept. of ECE Dhaka Bangladesh George Mason University Dept. of Computational Data Science FairfaxVA United States American International University-Bangladesh Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798331506520

In contemporary diagnostic workflows, medical image captioning has emerged as a pivotal advancement, combining deep learning methodologies and transformer architectures to enhance accuracy and efficiency in medical interpretations. This paper proposes the optimization of multimodal transformers for automated medical image captioning, focusing on integrating Vision Transformers (ViT) and Bidirectional Auto-Regressive Transformers (BART) with novel variations such as Swin Transformers and GPT-2. We use a robust multimodal AI framework to explore how these architectures synergize to generate coherent and diagnostically relevant captions for radiological images. We assess the performance of multiple transformer models by employing the ROCO dataset, containing paired X-ray images and expert-generated reports. Our findings demonstrate that the ViT + BART combination yields the most stable and accurate captions, minimizing training and validation loss. In contrast, DEiT + MBART displayed instability, highlighting the need for further hyperparameter tuning. Through this comparative analysis, we underscore the critical role of transformer-based models in reducing the cognitive load on medical professionals, enhancing diagnostic accuracy, and promoting real-time, automated medical image interpretation. © 2025 ieee.

关键词： Medical imaging

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