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Detection of Military Vehicles based on Edge Devices from Unmanned Aerial Vehicle 2

Detection of Military Vehicles based on Edge Devices from Un...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Umasankar, L. Anitha, D. Trisheela, S. Al-Jawahry, Hassan M. Borra, Subba Reddy S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India The Islamic University Najaf Iraq Computer Science and Engineering Maisammaguda Hyderabad India

ISBN: (纸本)9798350382952

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design. © 2024 IEEE.

关键词： Military vehicles

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

引用

1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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CMM PLN at MentalRiskES: A Traditional Machine Learning Approach for Detection of Eating Disorders and Depression in Chat Messages

CMM PLN at MentalRiskES: A Traditional Machine Learning Appr...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Guerra, Rodrigo Pizarro, Benjamín Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Dunstan, Jocelyn Center for Mathematical Modeling Universidad de Chile Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile National Center for Artificial Intelligence Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Millenium Institute Foundational Research on Data Chile

This paper describes our approaches to solving the MentalRiskES task, which belongs to the IberLEF (Iberian Languages Evaluation Forum) shared task. The task aims to identify the eating disorders and depression of a user using a series of Telegram messages. Our proposed system uses the traditional TFiDF method to represent the messages and then utilizes these representations as input for machine learning models. The best results for classification were obtained using the Naive Bayes classifier, while in the regression task, the best models were Gradient Boots Regressor and Linear Regressor. Despite its simplicity, we demonstrated that our traditional approaches can still achieve competitive results in recent NLP tasks, obtaining the best results in the case of detecting depression and eating disorders. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Natural language processing systems

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IoT-Enhanced Path Planning and Obstacle Avoidance for Autonomous Mobile Robots 3

IoT-Enhanced Path Planning and Obstacle Avoidance for Autono...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Selvanathan, N. Ramya, S.R. Sureshkumar, C. Kiruthiga, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Sona College of Technology Department of Information Technology Salem India Dr NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of AI&DS Erode India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350354171

Path planning and obstacle avoidance play a key role in how well autonomous mobile robots work and stay safe when they move through tricky areas. Traditional path-planning methods often can't keep up when new obstacles pop up or existing ones change, which makes them less useful in the real world. To fix these problems, the proposed research work brings in a new IoT-powered path planning and obstacle avoidance system that uses the D∗ Lite and Deep Reinforcement Learning (DRL) algorithms. Our approach brings together D∗ Lite's quick replanning skills with DRL's ability to learn and adapt made even better by live data from IoT sensors. We've put our model through its paces in different simulated environments set up to be as tricky as real-world navigation. Quantitative analysis shows it's a big step up from older models. Adding IoT data leads to a 30% boost in how paths are planned and cut the time robots need to get through complex routes by 20%. Plus, using DRL helps robots deal with moving obstacles 25% better, which means fewer potential crashes. This is a big step forward in the field of autonomous robotics. It gives a robust answer that would not just be reactive to the given environmental inputs but also, through proactivity, prompt possible obstacles, further contributing to smoother and safer output. As we move along with the development of our methodology, we foresee broad applications in many sectors enhancing the autonomy and efficiency of robotic systems working in dynamic and unpredictable environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Artificial Intelligence and Cyber Security in Industry 4.0 1

引用

丛书名： Advanced Technologies and Societal Change

2023年

作者： Velliangiri Sarveshwaran Joy Iong-Zong Chen Danilo Pelusi

ISBN: (数字)9789819921157

ISBN: (纸本)9789819921140;9789819921171

关键词： Artificial Intelligence Cyber-physical systems, IoT Professional Computing Big Data Machine Learning computational Intelligence

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Design and Performance Analysis of a Solar Powered Dough Maker 7

Design and Performance Analysis of a Solar Powered Dough Mak...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Periyasamy, Muthuvel Pitchaimanickam, B. Dheepanchakkravarthy, Azhagesan Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnan Koil626126 India Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankolathur603203 India Bannari Amman Institute of Technology Sathi Department of Electrical and Electronics Engineering Erode India

ISBN: (纸本)9798350397284

All the domestic loads in the kitchen are powered by AC supply. Usages of domestic loads are drastically increasing each day and the power needs are also increasing, but generation of power is not sufficient to meet the demand. Because of the non-availability of the fuel and coal, the world focuses on generating power from renewable energy resources. Renewable energy in general and Photovoltaic (PV) in particular are generating DC supply. Hence, PV cannot be directly used, because our domestic loads are AC. DC to AC convertion causes losses and the efficiency is reduced. This system is proposed which comprises a PV generator, a Permanent Magnet Direct Current motor for driving a dough maker smart system for automated motion which can be controlled by any Smart phone. The main part of our system architecture has a micro-controller Atmega328P which drives the various directions of the dc motor for forward and reverse motion of the dough maker. Also, the proposed system is analyzed to obtain the performance ratio and average efficiencies for total system. Further, obtaining the optimum PV array size based on the Life Cycle Cost analysis for the dough maker is presented. The complete operation and implementation of this scheme is also shown and steady state results are presented. © 2023 IEEE.

关键词： Solar energy

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Brain Graph Super-Resolution for Enhanced Neurological Disorder Diagnosis

Brain Graph Super-Resolution for Enhanced Neurological Disor...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M.S. Roobini M. Samuel Jebaraj P.B. Sasirudh K. Babu M. Nafees Muneera Department of Computer Science and Engineering Sathvabama Institute of Science and Technology Chennai Department of Computational Intelligence SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Functional brain data analysis is important in diagnosing neurological disorders, yet constructing high-resolution (HR) brain graphs from low-resolution (LR) although the data presents some computational challenges. This paper introduces the Brain Graph Super-Resolution (BGSR) method that leverages generative adversarial networks (GANs) to directly generate HR graphs from LR data. By bypassing complex image processes, BGSR enhances diagnostic accuracy efficiently while retaining the features. This paper explains BGSR’ s methodology, detailing its implementation steps and emphasizing its potential to revolutionize neurological disorder diagnosis. Experimental results demonstrate BGSR's effectiveness in improving diagnostic precision compared to traditional methods. Furthermore, itdiscusses the broader implications of BGSR in advancing functional brain data analysis and suggests future research directions for further works and application in the field of medicine. BGSR is a promising tool for researchers and clinicians alike, offering insights into the minute workings of the brain and facilitating more accurate diagnosis and treatment of neurological disorders. Through its innovative approach, BGSR contributes to the ongoing efforts to unravel the complexities of the human brain and offers new avenues for understanding and addressing neurological disorders. Its streamlined methodology and enhanced diagnostic capabilities hold promise for improving patient outcomes and advancing the field of neuroscience. In the realm of neurological disorder analysis, conventional methods often grapple with issues of precision and struggle to cope with the brain data due to its complexity. This paper introduces a novel paradigm known as Brain Graph Super-Resolution (BGSR), a pioneering technique leveraging the potential of Generative Adversarial Networks (GANs) to synthesize high-resolution brain graphs directly from low-resolution data, by circumventing convoluted image processing wo

关键词： Neurological diseases Data analysis Accuracy Neuroscience Precision medicine Instruments Superresolution Generative adversarial networks Complexity theory Medical diagnostic imaging

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Prediction of Rental Demands using different Machine Learning Algorithms

Prediction of Rental Demands using different Machine Learnin...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, S. Vijay, K. Pavithra, S. Prithi, S. Kannan, Sriram Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineeirng Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Chennai India SRM IST School of Computing Department of Computational Intelligence Kattankulathur India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348217

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions. © 2023 IEEE.

关键词： Machine learning

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Hourly Journal: A Mobile App for Optimizing Your Daily Routines 7

Hourly Journal: A Mobile App for Optimizing Your Daily Routi...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sureshkumar, S. Suryavarshini, S. Chellammal, P. Murugan, K R. Senthil Rani, P. Kavitha Kaviyaraj, R. J.J College of Engineering and Technology CSE Trichy India Sri Krishna College of Engineering and Technology CSE Coimbatore India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350397253

There are certain apps to record how people spent their day and whether they lived the day as per their goals. But these only make people feel guilty for having wasted the day or happy on being productive, but no app shows what is the pattern for success and failure. But with Hourly Journal it is possible to understand the pattern of daily time investments and fix it, empower it and grow in life overall. Mobile application development is becoming more popular due to the expanding popularity of smartphones and tablets. Traditional software development has had a significant influence on the development of mobile apps. However, the ultimate product is software designed to take use of mobile devices' specific capabilities and technology. To solve the problem of performance on each specific device, you must first design an app for that device. This implies writing code that is tailored to the exact hardware of a certain device. In the case of iOS devices, this is simple since mobile developers simply need to create apps for the iPhone and iPad to make their apps universally usable. For Android devices, on the other hand, the hardware and operating system versions vary from device to device. With the Hourly Journal available in the palm of your hand, people can use it as a modern-day diary, with revolutionary futuristic insights into the patterns of people's activities and tendencies. Based on tag tracking there is scope for human behavioral prediction in future too. © 2023 IEEE.

关键词： Software design

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