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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Murali N D. Beulah David Department of Computer Science and Engineering SIMATS Saveetha School of Engineering Chennai Tamilnadu India Department of Data Analytics SIMATS Institute of Information Technology Saveetha School of Engineering Chennai Tamilnadu India

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design.

关键词： Machine learning algorithms Urban areas Sociology Neural networks Machine learning Traffic control Internet of Things

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Applications of Artificial Intelligence in Biomedical Image Processing 2

Applications of Artificial Intelligence in Biomedical Image ...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Rajeyyagari, Sivaram Hung, Bui Thanh Chakrabarti, Prasun Shaqra University College of Computing and Information Technology Department of Computer Science Shaqra Saudi Arabia Thu Dau Mot University Data Analytics and Artificial Intelligence Laboratory Engineering Technology Faculty Thu Dau Mot Viet Nam Provost and Institute Endowed Distinguished Senior Chair Professor Techno India Njr Institute of Technology Rajasthan India

ISBN: (纸本)9781665400527

The biomedical applications of nanoparticles are growing in recent years. The uses range from diagnostic to therapeutic purposes, and researchers are constantly introducing new ways. Biosensors, miniaturization techniques, surface enhancement in implantable biomaterials, and better nanofibers in medical textiles are some of the contributions of biomedical nanotechnology. Therapeutic (medical) imaging successfully maps the anatomy of the human body. Image enhancement, analysis, categorization, and image based representation are all possible areas of computer processing and analysis of medical images. Therapeutic image understanding uses computer vision, applied mathematics, signal analysis, and artificial intelligence to recognise objects in medical images. Image processing is the use of computer algorithms to manipulate digital images. Hand-held transducers emit and receive pulsed acoustic waves for medical ultrasound imaging. In this study, a computer-aided diagnosis method is developed to help cytotechnicians locate and classify aberrant cells in cervical cytology images. With the outcomes of morphological and statistical edge based segmentation are compared to traditional segmentation techniques. The proposed segmentation methodology outperforms the other methods. This proposed technique's segmentation accuracy is 98%. © 2022 IEEE.

关键词： Artificial intelligence

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A Comparison of the Instructor-Trainee Dance dataset Using Cosine similarity, Euclidean distance, and Angular difference

A Comparison of the Instructor-Trainee Dance Dataset Using C...

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International computer science and engineering Conference (ICSEC)

作者： Thanawat Srikaewsiew Khatadet Khianchainat Atima Tharatipyakul Suporn Pongnumkul Sarunya Kanjanawattana Department of Computer Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center (NECTEC) Pathumthani Thailand

ISBN: (纸本)9781665462730

The COVID-19 outbreak has restricted most outdoor activities, leads to increasing interest in exercise at home with online trainers. One issue of online exercise technology is the safety since improper motion might result in injury. As a basis to prevent improper motion, methods for evaluating the motion similarity between an instructor and a trainee are essential. Cosine similarity, Angular difference, and Euclidean distance are three general ways for the motion evaluation. This study aimed to determine the most effective way for analyzing the similarity of human motion on the dataset of instructor-led dances. We first experimented with the data to find the appropriate cut-off value for classifying posture into two classes based on the similarity score. Confusion matrix, precision, recall, F1-score, accuracy of the results were then used to compare the efficiency. We discovered that Cosine similarity had the highest accuracy, 82.77 percent at cut-off 93.

关键词： computer science COVID-19 Humanities Euclidean distance Safety Injuries

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Development and Innovations in Wearable Sensors and Devices for Human Machine Interaction in Virtual Platform 2

Development and Innovations in Wearable Sensors and Devices ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Singh, Rahul Javali, Anand Priya, S. Vardhana Naidu, B Vishnu Gautam, Gaurav Presidium Gurgaon Sec-57 Pgt Physics Department of Physics Haryana 122103 India Conduent Technology Services - Usa Data Analytics and Innovations Dept FriscoTX75035 United States Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Mohan Babu University Department of Mechanical Engineering Andhra Pradesh Tirupati India Graphic Era Hill University Department of Physics Dehradun India

ISBN: (纸本)9798350325799

Recent advances in deep learning algorithms and wearable sensor technology have opened the door for creative methods to improve human-machine interaction (HMI) on virtual platforms. The use of wearable sensors and devices in conjunction with hybrid deep learning algorithms is presented in detail in this abstract. Real-time physiological and contextual data from users can be collected with the help of wearable sensors integrated into devices like smartwatches, augmented reality goggles, and biometric monitoring equipment. This information can then be used to improve augmented reality (AR) and virtual reality (VR) systems, enabling more effortless and intuitive user interactions. A seamless connection between the user and the virtual platform is made possible by the combination of sensor data and deep learning models, which enables the creation of complex algorithms that can decipher user intent, gestures, emotions, and physical actions. These hybrid models may efficiently learn complicated patterns from wearable sensor data by combining standard feature extraction techniques with convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Wearable sensors and hybrid deep learning techniques have numerous and significant applications in virtual platforms. They make it possible for, among other things, real-time activity recognition for individualized virtual fitness coaching, emotion recognition for adaptive and sympathetic virtual characters, and gesture recognition for intuitive control of virtual objects. In summary, combining wearable sensors and devices with hybrid deep learning algorithms has enormous potential to change how humans and machines interact on virtual platforms. © 2023 IEEE.

关键词： Emotion Recognition

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A security framework for 5G-enabled wireless sensor networks based on blockchain technology

A security framework for 5G-enabled wireless sensor networks...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Durgarao, M.S.P. Valluru, Dinesh Saraswathi, S. Malathi, P. Thilagam, K. Loganathan, S. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India Mlritm Engineering College Department of Information Technology Hyderabad India Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Dept of Cse - Aiml Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamilnadu Chennai India Velammal Engineering College Department of Ece Tamil Nadu Chennai India Paavai Engineering College Department of Ece Tamilnadu Namakkal India

ISBN: (纸本)9798350359688

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results. © 2024 IEEE.

关键词： Smart contract

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Automation Through Machine Learning: Reinvigorating Cyber Security 15

Automation Through Machine Learning: Reinvigorating Cyber Se...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bagade, Jayashree V. Dari, Sukhvinder Singh Dhivrani, Rekha Chowhan, Santosh Ravivarman, G. Janakiraman, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Symbiosis International Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats. © 2024 IEEE.

关键词： Cyber Instructions Malicious Procedures Threats

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Study and Trend Prediction of Covid-19 cases in India using Deep Learning Techniques 1

Study and Trend Prediction of Covid-19 cases in India using ...

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1st International Conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Shrivastava, V.K. Kumar, Ajay Shrivastava, A. Tiwari, A. Thiru, K. Batra, R. Department of Big Data Analytics Adani Institute of Digital Technology Management Gujrat Gandhinagar382423 India Department of Mechanical Engineering SGT University Haryana Gurugram122505 India Department of Electronics and Tele Communication Engineering IIIT Odisha Bhubaneswar751003 India Department of Information Technology Madhav Institute of Technology and Science Gwalior474005 India United World School of Computational Intelligence Karnavati University Gujrat Gandhinagar383422 India Reenu Batra Department of Computer Science and Engineering SGT University Haryana Gurugram122505 India

The novel coronavirus or officially known as SARS-CoV 2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused a severe pandemic over the world affecting not only the economy of the countries but also the lifestyle of the people worldwide. As on 31.12.2020, Covid-19 (coronavirus disease) has infecting more than 10266674 people and causing about 148738 deaths in India. It has been seen through various statistics of various countries that the number of Covid-19 cases grows exponentially as the number of test increases then after some period, the rate of new cases decreases. In this research paper, researchers have created deep learning-based model to predict the curve of the new Covid-19 cases vs the total number of tests conducted in India. There is still lockdown in some part of the country while some states have partially relaxed the rules and some states totally lifted the lockdown. Predicting the number of new cases and their trend can help in deciding what is the optimal time to release the lockdown. It will also help in determining when the coronavirus will loosen its grip from India. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

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A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering

arXiv

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arXiv 2024年

作者： Song, Shuang Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Modeling outdoor scenes for the synthetic 3D environment requires the recovery of reflectance/albedo information from raw images, which is an ill-posed problem due to the complicated unmodeled physics in this process (e.g., indirect lighting, volume scattering, specular reflection). The problem remains unsolved in a practical context. The recovered albedo can facilitate model relighting and shading, which can further enhance the realism of rendered models and the applications of digital twins. Typically, photogrammetric 3D models simply take the source images as texture materials, which inherently embed unwanted lighting artifacts (at the time of capture) into the texture. Therefore, these "polluted" textures are suboptimal for a synthetic environment to enable realistic rendering. In addition, these embedded environmental lightings further bring challenges to photo-consistencies across different images that cause image-matching uncertainties. This paper presents a general image formation model for albedo recovery from typical aerial photogrammetric images under natural illuminations and derives the inverse model to resolve the albedo information through inverse rendering intrinsic image decomposition. Our approach builds on the fact that both the sun illumination and scene geometry are estimable in aerial photogrammetry, thus they can provide direct inputs for this ill-posed problem. This physics-based approach does not require additional input other than data acquired through the typical drone-based photogrammetric collection and was shown to favorably outperform existing approaches. We also demonstrate that the recovered albedo image can in turn improve typical image processing tasks in photogrammetry such as feature and dense matching, edge, and line extraction. [This work extends our prior work "A Novel Intrinsic Image Decomposition Method to Recover Albedo for Aerial Images in Photogrammetry Processing" in ISPRS Congress 2022]. The code will be made available

关键词： Inverse problems

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Point Cloud Registration for LiDAR and Photogrammetric data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms

arXiv

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arXiv 2023年

作者： Xu, Ningli Qin, Rongjun Song, Shuang Geospatial Data Analytics Lab The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Three-dimensional (3D) point cloud registration is a fundamental step for many 3D modeling and mapping applications. Existing approaches are highly disparate in the data source, scene complexity, and application, therefore the current practices in various point cloud registration tasks are still ad-hoc processes. Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or RealSense cameras), lacking a comprehensive overview of their applicability in photogrammetric 3D mapping scenarios. In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. The quantitative analysis allows for exploring the strengths, applicability, challenges, and future trends of these methods. In contrast to existing analysis works that introduce point cloud registration as a holistic process, our experimental analysis is based on its inherent two-step process to better comprehend these approaches including feature/keypoint-based initial coarse registration and dense fine registration through cloud-to-cloud (C2C) optimization. More than ten methods, including classic handcrafted, deep-learning-based feature correspondence, and robust C2C methods were tested. We observed that the success rate of most of the algorithms are fewer than 40% over the datasets we tested and there are still are large margin of improvement upon existing algorithms concerning 3D sparse corresopondence search, and the ability to register point clouds with complex geometry and occlusions. With the evaluated statistics on three datasets, we conclude the best-performing methods for each step and provi

关键词： Iterative methods

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