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Body Fat Prediction using Various Regression Techniques 2

Body Fat Prediction using Various Regression Techniques

引用

2nd IEEE international conference on advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Mahesh, Nikhil Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Engineering Oman

ISBN: (纸本)9798350315905

Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study's results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction. © 2023 IEEE.

关键词： Forecasting

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Boosting Practicality of Natural Image Matting with Trimap Adaptation 2nd

Boosting Practicality of Natural Image Matting with Trimap A...

引用

2nd international conference on the Frontiers of robotics and Software engineering, FRSE 2024

作者： Wang, Pengjun Zhu, Dongchen Gao, Yan Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819630127

Image matting is a visual technology that can be applied to robots for visual perception and environmental understanding. Natural image matting algorithms strive to predict the alpha matte with the assistance of trimaps. Nonetheless, the generation of trimaps poses a significant challenge, constraining their applicability in various practical contexts. This is because trimaps need to be manually created, and pixel-level foreground/background/unknown annotation involves high labor *** address the common practical problem, we introduce TAMatting, a general matting framework that converts trimap-guided methods to mask-guided approaches without additional training. It can be applied to most matting methods and attains comparable performance, making high-quality matting more accessible. The cornerstone of TAMatting lies in its trimap adaptation, an innovative procedure that utilizes a coarse mask as a guide to produce a precise trimap. Subsequently, a pre-trained matting model predicts alpha mattes with the generated trimaps. Experimental results demonstrate that our TAMatting is widely applicable in addressing practical challenges. Additionally, we offer qualitative results on practical applications, showing the significant impact and potential of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wages

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Unified Model of Entity Tracking Using Deep Learning

Unified Model of Entity Tracking Using Deep Learning

引用

2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Kumar, B Anand Karunambiga, K. Vinod, T. Buela, Elari Sudharani, N. Sowjanya, A. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore641105 India

ISBN: (纸本)9798350348217

The act of identifying a moving object inside a video is referred to as 'entity tracking.' It has applications in monitoring and robotics, as well as imaging medicine and tracking autonomous vehicles, among other areas. Single Object Tracking, Multiple Object Tracking, Video Object Segmentation, and Multi-Object Tracking and Segmentation are the four subtasks that make up the majority of the field of entity tracking. The vast majority of previously developed techniques are only produced for a single one or a portion of the subtasks. With the same set of model parameters, the unified model Unicorn is able to simultaneously solve all four different kinds of object tracking issues (SOT, MOT, VOS, and MOTS). The 'Unicorn' model that we are currently working to implement uses a single network to solve all four tracking problems to the same degree or a higher degree than its explicit undertaking partners in eight different tracking datasets. These datasets include LaSOT, Tracking Net, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. © 2023 IEEE.

关键词： Deep learning

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Monitoring Fukui Conical Cup Test Using Acoustic Emission

Monitoring Fukui Conical Cup Test Using Acoustic Emission

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robotics, engineering, science, and technology (RESTCON), international conference on

作者： Phimmat Kalawong Nopparat Seemuang Sansot Panich Komkamol Chongbunwatana Saiyan Primee Dept. of Production and Robotics Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Acoustic emission (AE) is a nondestructive testing technology has been used for monitoring cracks and deformation in several materials. In this study, AE features were used to verify crack formation during Fukui conical cup test of aluminum alloy grade AA2024-T3 sheet and DP440 dual-phase steel sheet. Additionally, the effects of lubricants on the AE signals were investigated. AE sensor, signal amplifier and a data acquisition system were used for data collection and LabView software was used for data processing and feature extraction. Features were extracted from the AE signal gained during this test, including maximum amplitude (AE-Max) and cumulative energy (AE-energy). Initial results suggested that some AE features clearly correlate with fractures and were employed to monitor events encountered during the Fukui conical cup test, utilizing simple AE features for analysis and validation. This ensures the AE technique can be used as monitoring tool during forming process of selected material sheets.

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EchoNet Dynamic: Deep Learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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Active Sampling Planning by Attention-Based Deep Neural Network for Environmental Field Mapping 5

Active Sampling Planning by Attention-Based Deep Neural Netw...

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5th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, Kun Cheng, Qingyuan Lai, Xiaohang Li, Teng School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350357950

Regarding the problem of environmental field reconstruction, a method is proposed to establish the optimal observation position of the environmental field using a random process and complete the online planning of the optimal observation position through deep learning technology. First, this chapter uses a random process method to construct an environmental field model and describes the environmental object as a Gaussian Markov process. For the variable spatial field random process model, the information gain evaluation mechanism is used to define the sampling point with the most information in the field as the approximate optimal observation position, and the global optimal observation point is generated. Subsequently, this chapter proposes a high-precision fitting representation and real-time calculation framework based on a deep attention network, constructs a deep neural network with complex nonlinear representation learning ability, learns the relationship between the covariance function corresponding to the Gaussian random field and the optimal information observation point, simulates the global optimal observation position generated by the random process environment model, solves the real-time calculation of the global optimal position, and realizes the online representation and planning of the spatial field observation position. © 2023 IEEE.

关键词： Active sampling Deep learning Environmental mapping Robotic sensing

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Beyond The Internet of Things: Imagining IoT's Structure and Potential Routes

Beyond The Internet of Things: Imagining IoT's Structure and...

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2023 international conference on advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Varalakshmi, K.R. Mahesh, T.R. Singh, Akhilendra Pratap Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

wireless sensor system technology has made it possible for ubiquitous sensing, which affects many aspects of contemporary life. Indicators of environmental health, from sensitive ecologies as well as biodiversity to urban settings, may now be measured, inferred, and understood. The Internet of Things is enabled by the extensive utilization of these devices in a network that facilitates communication and action. In this network, the actuators and sensors smoothly interact with our environment as data is exchanged across platforms to provide a unified operational view. Near field communication (NFC) is the upcoming technology that will enable the transformation of the worldwide web into a fully integrated Future Internet. This technology is being advanced through the adoption of variousenabling device methods, such as tags, audience members, encased sense and actuators nodes, and nodes for near field interaction. The demand for data-on-demand, using complex and straightforward queries, increases significantly as studies transition from web to web2 to web3. This study presents a cloud-centric vision for the worldwideimplementation of the Internet of Things. The future of IoT research is expected to be influenced by many significant technology enablers and their corresponding application domains, which are emphasized. A cloud architecture based on the interplay of public as well as private clouds and employing Aneka is shown. Study conducted elaborate on the need of unity, the global web, and distributed processing in our last section of our IoT vision for the community of technical researchers. © 2023 IEEE.

关键词： Cloud-centric Internet of Things Data storage and analytics Internet of Things (IoT) New protocols Participatory Sensing Quality of Service (QoS) Radio Frequency Identification (RFID) User-cantered cloud-based paradigm Visualization Wireless Sensor Networks

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Federated Learning and Decentralized IoT Cooperation

Federated Learning and Decentralized IoT Cooperation

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2023 international conference on advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Kumar, Dinesh Krishna, Vijaya Manju Bargavi, S.K. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

In an effort to lessen the difficulties caused by large-scale transfer of data on mobile phone networks, edge buffering tech has emerged as a result of the introduction of Internet of Things (IoT) apps by developers and carriers. Current optimization-based methods often struggle to adjust to changing circumstances. In order to solve these problems, most modern learning-based technologies are presented in a centralized way, which wastes network resources on refresher as well as data sharing. Study conducted present the Coordinated Deep Regeneration Edge Caching System (FADE) in the above setting. FADE enables decentralized cooperative development of a common prediction model among base stations. Base stations start local education with their initial sets of parameters and then upload almost perfect local circumstances to take part in international training sessions. Study conducted prove that convergence of FADE, displaying the intended convergence features. FADE demonstrates a considerable improvement over centralized deep training and reinforced approaches via trace-driven trials, resulting in a 92% decrease in performance deterioration and a mean 60% lower system costs. Especially, it outperforms the state-of-the-art methods in this field. © 2023 IEEE.

关键词： Content Replacement Cooperative Edge Caching Decentralized IoT Cooperation Deep Reinforcement Learning Edge Caching FADE (Federated Deep renewal learning-based cooperating Edge caching) Federated Learning Internet of Things (IoT) QoS (Quality of Service) and System Payment

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Linkdb-TCP: A Congestion Control Technique For MANET Based On Link Layer Measurements 1

Linkdb-TCP: A Congestion Control Technique For MANET Based O...

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1st IEEE international conference on advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Sreenivasa, B.C. Bhanu Prakash, G.C. Sir M Visvesvaraya Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350348057

Detection and control of congestion in Mobile ad-hoc networks (MANET) is a challenging task. Congestion hurts performance and reduces the throughput of the system. Thus it is imperative to avoid or control congestion in the network. Congestion control algorithms provided by the Transmission Control Protocol (TCP) is specially designed for wired communication and performs poorly in the case of ad-hoc networks. This paper presents a Link-Layer Congestion control algorithm for MANETs, which detects congestion in the network by carrying out an end-to-end analysis. The congestion window is appropriately modified if congestion persists in the system. The new congestion window is communicated to the sender, and its behavior is altered accordingly. The algorithm can be seamlessly incorporated into TCP for practical use © 2023 IEEE.

关键词： Transmission control protocol

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Design of Hybrid Model Based on Chaotic Map to Secure the Images 1

Design of Hybrid Model Based on Chaotic Map to Secure the Im...

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1st international conference on Recent advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Amjed, Afiya Kumar, Jalesh Jawaharlal Nehru New College of Engineering Dept. of Computer Science & Engineering Karnataka Shivamogga India

ISBN: (纸本)9798350306637

Secure multimedia transmission is one of the challenging conditions in the age of communication technology. To achieve a secure multimedia transaction encryption can be used which preserves the confidentiality, integrity, and authenticity of multimedia content (such as images, videos, and audio) during digital transactions. Encryption techniques that transform original image into an unintelligible or scrambled form, known as a cipher image, are one way to achieve secure image transmission over the network. This way, even if the attacker gains access to the cipher, they would not be able to retrieve the real image. Chaos-based encryption is a cryptographic technique that uses chaotic systems inherent unpredictability and sensitivity to beginning conditions to generate encryption algorithms. This study enables the implementation and analysis of chaos-based picture encryption algorithms for image encryption. A chaotic system, such as the logistic map or the Lorenz system, is used to generate chaotic value sequences. These systems are extremely sensitive to beginning conditions, making it difficult to predict their future states. As a native chaotic and hybrid chaotic techniques, Arnold cat map, Henon map and Tent map are utilised in security models this offers an option for improving the protection of multimedia information in an increasingly linked environment. The experimental results shows that the proposed system work satisfactorily. © 2023 IEEE.

关键词： Cryptography

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