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Real-Time Food Detection and Nutritional Tracking Application for Personalized Health Management Using MobileNetV2 8

Real-Time Food Detection and Nutritional Tracking Applicatio...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Ganpisetty, Divyabhavani Reddy, Chetana R Ganpisetty, Navyashree Anitha, J. Rv Institute of Technology and Management Dept of Computer Science and Engineering Bengaluru India Rv Institute of Technology and Management Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331505462

Accurate and timely dietary assessment is essential for health management. This study presents a deep learning-based approach for real-time, personalized food analysis through image analysis. The preparation processaccurately predicts the content of food products based on images captured by a smartphone camera. The model is trained on a comprehensive database of food images along with their corresponding menu items. To enhance user experience and engagement, the system integrates a mobile application that allows users to easily record their meals by taking photos. Test resultsshow the system's effectiveness in accurately estimating macronutrients and calories, providing users with a better understanding of making dietary decisions. This research contributes to the development of smart health devices and has the potential to impact public health through health promotion. © 2024 IEEE.

关键词： computer vision deep learning food recognition

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computer Vision based Panoptic Driving Perception under Various Weather Conditions

Computer Vision based Panoptic Driving Perception under Vari...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Sridevi, M. Harish, M. Department of Computer Science & Engineering National Institute of Technology Tiruchirappalli620015 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Driving Perception has become an inevitable aspect of autonomous driving systems which demands a very good accuracy and speed. Over a decade, various multi-tasking models have been proposed to simultaneously carry out traffic object detection, drivable area segmentation, and lane detection. Among all of them, YOLOP, YOLOPv, and HybridNets are the 3 models that have accomplished the above tasks using the BDD100K Dataset with high excellent accuracy and efficiency, thus achieving the new State-Of-The-Art (SOTA) performance. This paper focuses mainly on comparing the performance of the above-mentioned models in various weather conditions based on accuracy, robustness, and computational speed. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Object detection

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Open-Source Visual Target-Tracking System Both on Simulation Environment and Real Unmanned Aerial Vehicles 2nd

Open-Source Visual Target-Tracking System Both on Simulation...

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2nd International Congress of Electrical and computer engineering, ICECENG 2023

作者： Yılmaz, Celil Ozgun, Abdulkadir Erol, Berat Alper Gumus, Abdurrahman Electrical and Electronics Engineering Izmir Institute of Technology Izmir Turkey Computer Engineering Izmir Institute of Technology Izmir Turkey Meshine Swarm Technologies Izmir Turkey

ISBN: (纸本)9783031527593

This work presents an investigation into the domain of dynamic target tracking through object detection, particularly emphasizing the context of open-source applications like PX4, ROS, and YOLO. Over the years, achieving real-time object tracking on UAVs in dynamic environments has been a formidable challenge, necessitating offline computations or substantial onboard processing resources. However, contemporary UAVs are now equipped with advanced edge embedded devices, sensors, and cameras, enabling the integration of deep learning-based vision applications. This advancement offers the prospect of directly deploying cutting-edge applications onto UAVs, thereby expanding their utility in areas such as surveillance, search and rescue, and videography. To fully harness the potential of these vision applications, a communication infrastructure interfacing with the UAV’s underneath closed controllers becomes imperative. We’ve developed an integrated visual target-tracking system that connects a flight controller unit with a graphical unit by leveraging ROS tools and open-source deep learning packages. The overall integrated system based on ROS, deep learning applications, and custom PID controllers is shared on GitHub as open-source software package in a way that benefits everyone interested: https://***/miralab-ai/vision-ROS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Unveiling Sentiments: Exploring Tweet and Text Analysis for Deeper Insights

Unveiling Sentiments: Exploring Tweet and Text Analysis for ...

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ranjan, Ritik Karthik, N. Vani, V. National Institute of Technology Puducherry Department of Electrical and Electronics Engineering Karaikal India National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

This work presents a comprehensive exploration of sentiment analysis on social media data, with a specific focus on Twitter. The study evaluates three state-of-the-art classifiers-Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF)-to categorize sentiments (positive, negative, neutral) based on specific criteria. Data cleansing and classification methods are applied to extract meaningful insights from unstructured social media data. Results show that SVM achieves the highest accuracy (92.67%) among the classifiers. It also discusses the strengths and limitations of each classifier in capturing the nuances of sentiment expressions. This work contributes to the understanding of sentiment analysis techniques on social media platforms © 2024 IEEE.

关键词： Tweets

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Combat with Class Overlapping in Software Defect Prediction Using Neighbourhood Metric

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SN computer Science 2023年第5期4卷 1-10页

作者： Gupta, Shivani Richa Kumar, Ranjeet Jain, Kusum Lata School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer & Communication Engineering Manipal University Jaipur Jaipur India

The characteristics of data is a open problem which has been tended perceived in data analysis in machine learning research from last decades. The researcher defined some measures to identify the characteristics of the dataset by applying data complexity measures to find the fitness for purpose. The presence of class overlapping in data-sets, significantly affect performance of the classifiers. Data complexity measures provide quantitative insight in quality of the data set and overlapping existent in it. Machine learning techniques are also utilized by several researchers on healthcare datasets in software defect prediction. In this paper, our aim is to evaluates the effectiveness of new overlap measure: Near Enemy Ratio, and its effect on complexity measures and performance of the classifier. The new ration is based on nearest instances to the target instance. The experimental result offers insights in usefulness of the method and help us decide whether this solution should be applied on a particular data-set or not. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC Class imbalance Class overlap G-mean Recall Software defect prediction

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Recent Advancement of Artificial Intelligence in COVID-19: Prediction, Diagnosis, Monitoring, and Drug Development 2nd

Recent Advancement of Artificial Intelligence in COVID-19: P...

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2nd International Conference on Women Researchers in electronics and Computing, WREC 2023

作者： Bachan, Priya Rachel Bera, Udit Narayan Kapoor, Prakriti Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India Electronics and Communication Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India

ISBN: (纸本)9789819970766

This paper intends to summarize the important applications of Artificial Intelligence and Machine Learning during the COVID-19 outbreak. In recent past, world has seen a massive pandemic due to COVID-19 virus outbreak, which affected millions of people across all nations. There have been tremendous implications and applications of Artificial Intelligence and Machine Learning, along with deep learning and Neural Networks, in the field of medical science, which has expanded the traditional dynamics of medical science and enhanced the decision-making as well as treatment delivery to the patients. Artificial Intelligence- and Machine Learning-based models have provided tools in numerous fronts, through which this pandemic can be overcome. In this paper, we have emphasized on the various important applications of Artificial Intelligence- and Machine Learning-based algorithms and models in the medical field for predicting the number of COVID-19-affected patients, diagnosing and detecting the virus, monitoring its spread as well as development of drugs and vaccines. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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DESIGN OF RF FEEDING NETWORK [1:8] USING UNEQUAL AMPLITUDE HYBRID COUPLER FOR LOW SLL X-BAND RADAR APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 1-19页

作者： Kothapudi, Venkata Kishore Sunayana, Lella Divya, Shaik Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St. ColumbiaSC29204 United States

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor’s amplitude = –17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end. © 2025 by Begell House, Inc.

关键词： Transmitters

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Advancing Liver Cancer Diagnosis: Innovative Deep Learning Algorithms for Segmentation and Classification

Advancing Liver Cancer Diagnosis: Innovative Deep Learning A...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Suganthi, B. Vidhya, U. Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798331541170

Liver cancer is a significant worldwide health issue that requires precise diagnosis techniques and efficient treatment options. This paper presents sophisticated deep learning (DL) algorithms for the segmentation and classification of liver cancers, providing a thorough comparative comparison. The advancements in DL have led to significant progress in other domains. As a result, there is a rising excitement for using DL techniques in liver research to aid clinicians in diagnosing and treating medical conditions. The Improved Fully Convolutional Neural Network (IFCNN) has been introduced in this study as a solution to the problem of liver tumor segmentation in cases of liver cancer. The IFCNN has been mathematically designed to address this issue. Transfer learning improves the ability to apply knowledge gained from one patient group to other patient groups with different characteristics. Comparative studies performed against ResNet and VGG16 demonstrate higher sensitivity, specificity, and overall classification metrics. The algorithms' efficacy and real-time processing capabilities have been extensively validated on multiple datasets, including different kinds and stages of liver cancer. This paves the way for eventual incorporation into clinical processes. These advanced algorithms exhibit superior precision and efficiency compared to current models, offering significant potential for improving clinical practice. They provide more dependable tools for precise diagnosis and informed treatment planning in the fight against liver cancer. © 2024 IEEE.

关键词： Convolutional neural networks

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Digital Twin-based Flight Dynamics Simulation

Digital Twin-based Flight Dynamics Simulation

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Talluri, Geetam Konjengbam, Anand Indian Institute of Technology Dharwad Dept of Electrical Electronics & Communication Engineering Dharwad India Indian Institute of Technology Dharwad Computer Science & Engineering Dharwad India

ISBN: (纸本)9798331505776

Unmanned Aerial Vehicle (UAV) systems face significant challenges during critical flight phases, such as post-liftoff stabilization and mid-flight disturbance response. This paper presents a Virtual Reality (VR) based simulation and control system integrated with a digital twin to enhance the understanding of UAV dynamics. The system uses real-time data from the UAV's Onboard computer (OBC) to create a dynamic 3D environment where users can interact with flight parameters such as position, rotational dynamics, and vibration amplitude. This approach enables the analysis of UAV performance and stability under changing conditions by allowing real-time adjustments. This immersive system supports research, training, and operational optimization of aerospace systems. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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