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检索条件"机构=Computer Science And Medical Engineering Faculty of Engineering and Technology"

共 38309 条记录，以下是4511-4520 订阅

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Enhanced Gesture Recognition Through Graph-Based Multimodal Fusion 14

Enhanced Gesture Recognition Through Graph-Based Multimodal ...

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Rehman, Mobeen Ur Ilyas, Talha Seneviratne, Lakmal Hussain, Irfan Khalifa University KU Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates Clayton 3168 Monash University Monash Medical AI Group Faculty of Engineering and Computer Science VIC Australia

ISBN: (纸本)9798350366556

This study introduces an advanced framework for recognizing hand gestures from a first-person view, leveraging the integration of multimodal data including optical flow, pose, depth, and RGB video recordings. Adeptly navigating the challenges and opportunities presented by the integration of multimodal data. At its core, the framework employs two pivotal components: a cross-attention based adaptive graph convolutional network and relational graph interactions for modality fusion. The former is designed to extract features from skeleton-based gesture data, ensuring a nuanced capture of hand movements by emphasizing the interconnections within the hand's skeletal structure. The latter component innovatively models each output modality feature as a node in a fully connected relational graph, facilitating the fusion of heterogeneous data types through dynamic interactions between modalities. This approach allows for the leveraging of each data type's strengths and the mitigation of their weaknesses, significantly enhancing the system's classification accuracy and robustness. Tested on a public benchmark dataset, the framework achieved a remarkable accuracy of 98.48%, demonstrating its efficacy. Moreover, it proves resilient, maintaining strong performance (93.48% accuracy) even in scenarios where only one modality is available, highlighting its potential for real-world applications. This advancement sets a new benchmark in hand gesture recognition, promising future developments in multimodal data fusion. © 2024 IEEE.

关键词： Musculoskeletal system

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Assessing the Efficiency of AI for Multivariate Data Analysis 3

Assessing the Efficiency of AI for Multivariate Data Analysi...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Jambi Ratna Raja Chaturvedi, Mukesh Anandhasilambarasan, D. Ashok, V. Nawadkar, Ashwini R. Va, Manoj Genba Sopanrao Moze College of Engineering Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

This study aimed to evaluate the efficiency of synthetic Intelligence (AI) for multivariate records analysis. Multivariate statistics evaluation is a complicated venture that historically requires considerable manual labor and information in information science and machine mastering. AI can offer an extra green and accurate method to the venture. Consequently, this study employed a ramification of AI-based total methods to identify patterns and generate insights from the dataset. Traditional and ML algorithms were used to generate models based on the records. The models have been evaluated based on their metrics - accuracy, precision, and keep in mind. In the end, the effects from the models were analyzed to identify trends and institutions among the variables. The findings concluded that AI may want to provide a robust and accurate technique to perform multivariate records analysis. However, further research is needed to develop novel tactics to utilize AI for multivariate statistics evaluation with increased accuracy and precision. © 2023 IEEE.

关键词： considerable multivariate ramification statistics evaluation synthetic

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AWS Server Less Luggage Tracking System Integrating IOT

AWS Server Less Luggage Tracking System Integrating IOT

引用

International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Aditi Maharjan Prabhesh Yadav Chulindra Rai Ajay Kumar Mandal Basavaraju D.R Computer Science and Engineering- Internet of Things Faculty of Engineering & Technology’ JAIN (Deemed-to-be University) Bengaluru Karnataka India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

The global airline industry serves over two billion traveller annually and faces continuous challenges about luggage handling. Lost luggage, mishandling of luggage are most common problem for the traveller. With the number of traveller even small percentage of baggage mishandling translates to significant number of effected *** system examines the current implementation of Amazon Web Services (AWS) and Radio Frequency Identification (RFID) in in luggage handling and proposes the adoption of Internet of Things (IoT) technology to develop reusable luggage tags. The main goal of this paper is to explore the problems and limitations in existing luggage handling systems and propose and IoT-based solution to enhance the baggage tracking efficiency. Existing systems did not provide the facility to name the luggage in the application or end-user interfaceThe research focuses on three main goals: evaluating the effectiveness of current RFID and AWS-based systems, developing a reusable IoT-enabled luggage tag and also evaluating the impact on reducing mishandling of luggage. The proposed system integrates smart, reusable tags with IoT capabilities allowing real time tracking of the *** proposed system demonstrated that the IoT based luggage tracking system outperformed RFID systems in terms of tracking efficiency and accuracy. Overall, integrating IoT technology into luggage tracking systems provides a better solution to the challenges faced by the passenger of air travel. The system also reduces the changes of lost luggage incidents and promotes environmental sustainability by the use of reusable tags.

关键词： Wireless communication Web services Green products Control systems Real-time systems Internet of Things Servers Faces Radiofrequency identification Airline industry

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Comparison of Functionality of Non-Conventional Instrument Transformers and Conventional Current Transformers in Distribution Networks 5

Comparison of Functionality of Non-Conventional Instrument T...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Burgund, Davorin Nikolovski, Srete Elektrogen Ltd. Zagreb Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In the development of electric, electronic, and information technologies in the medium and low voltage plants, designers reach out the moment of decision when it is necessary to choose equipment and systems that will be optimal for the set requirements. The smart grid concept in the power industry, especially at medium and low voltage, imposes the choice of equipment that will provide more data. The result of this development is the appearance of non-conventional instrument transformers and voltage sensors at medium and low voltage. Their dimensions, price, and functionality fit into these trends. Non-conventional instrument transformers (NCIT) and voltage sensors (VS) compete with conventional current transformers (CCT) and voltage transformers (VT). The paper discusses the specifics of replacing conventional with non-conventional instrument transformers and compares the measurement characteristics of both types, functionality, advantages, and disadvantages of both solutions. © 2022 IEEE.

关键词： Electric power distribution

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images 2

A Systematic Review of Deep Learning Approaches for Vessel S...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Hegde, Govardhan Prabhu, Srikanth Gupta, Shourya Prabhu, Gautham Manuru Palorkar, Anshita Srujan, Metta Venkata Bhandary, Sulatha V Department of Computer Science and Engineering India Department of Data Science and Computer Applications India Department of Information and Communication Technology Manipal Institute of Technology Karnataka Manipal576104 India Department of Ophthalmology Kasturba Medical College Karnataka Manipal576104 India

Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Design of a Method Using Machine Learning Techniques for the Maintenance and Diagnosis of Failures in Solar Panels

Design of a Method Using Machine Learning Techniques for the...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chaturvedi, Abhay Lohumi, Yogesh Patil, Harshal Sharath, N. Vekariya, Daxa Taqui, Syed Noeman Gla University Department of Electronics and Communication Engineering Uttar Pradesh Mathura281406 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Balaji University Maharashtra Pune India Bgs Institute of Technology Adichunchanagiri University Department of Mechanical Engineering BG Nagar Mandya Karnataka571448 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat 391760 India Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Saveetha School of Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

The increasing adoption of solar energy demands efficient maintenance strategies to ensure optimal performance and longevity of solar panel installations. This paper proposes a novel method leveraging machine learning techniques for the proactive maintenance and accurate diagnosis of failures in solar panels. The objective is to enhance the reliability and efficiency of solar energy systems while minimizing downtime and operational costs. The proposed method aims to empower solar panel operators with a proactive and intelligent maintenance system, allowing them to address potential issues before they escalate. By minimizing downtime and optimizing performance, this approach contributes to the sustainability and economic viability of solar energy installations. The effectiveness of the proposed method is demonstrated through experimental results and comparisons with traditional maintenance approaches. © 2024 IEEE.

关键词： Machine learning

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MyoRCB-CAM-Seg: A New Supervised Segmentation Mechanism for Diagnosis of Myositis medical ultrasound images Integrating new Modified Convolutional Residual Blocks and Convolutional Attention Mechanism

MyoRCB-CAM-Seg: A New Supervised Segmentation Mechanism for ...

引用

Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Alaa Hussien Sherin Youssef Noha Ghatwary Magdy A. Ahmed Computer Engineering Department Pharos University Alexandria Egypt Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Engineering Faculty of Engineering Alexandria University Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

Myositis is an extremely uncommon condition that causes muscle weakening due to an immune system attack on muscle cells. An accurate and timely diagnosis is essential for successful treatment. Differentiating among various kinds of myositis remains a big problem, leading to incorrect diagnoses and delayed treatment. Segmentation can improve the visualisation of medical ultrasound images by highlighting the region of interest and reducing the image's complexity by reducing the quantity of data that must be processed. This research presents a a unique supervised segmentation architecture that can efficiently learn from a fair amount of medical images to efficiently perform accurate segmentation tasks at low computing cost. Our model utilises a novel encoder-decoder architecture that incorporates an attention mechanism and GroupNormalizaton into a residual convolution block, allowing it to acquire and analyse image data at several resolutions within the encoder segment. On a benchmark set of myositis ultrasound pictures, the suggested method achieves state-of-the-art performance across all metrics including Precision, Recall, mean Dice coefficient, Jaccard index, and Accuracy. The proposed method exhibits high generalisation capabilities, producing outstanding results despite having access to a modest sample of training data. The proposed model achieved remarkable improvement in terms of accuracy compared to Unet++, DeepLabV3 and the original duck architecture which reached up to 12 %, 7 % and 2 % respectively. In addition, the proposed model also accomplished a noticeable improvement in terms of Dice Coefficient extended to 13 %, 11 % and 5 % respectively, when compared to the traditional models Unet++, DeepLab V3 and the original duck architecture respectively.

关键词： Measurement Image segmentation Accuracy Ultrasonic imaging Computational modeling Data visualization computer architecture

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Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Probabilistic Constellation Shaping for OFDM-Based ISAC Sign...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Du, Zhen Liu, Fan Xiong, Yifeng Han, Tony Xiao Yuan, Weijie Cui, Yuanhao Yao, Changhua Eldar, Yonina C. School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Ltd. Shenzhen China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot Israel

ISBN: (纸本)9798350370218

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations. © 2023 IEEE.

关键词： ambiguity function ISAC OFDM PCS

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Leveraging Reinforcement Learning to Improve Content Retrieval 3

Leveraging Reinforcement Learning to Improve Content Retriev...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Indira, G. Shelke, Ganesh C. Sharma, Bhuvnesh Singh, Vaishali Mahesh, T.R. Reddy, B. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Department of Information Technology Vishwakarma Institute of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Engineering Faculty of Engineering and Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Reinforcement studying (RL) is an artificial intelligence (AI) paradigm that can be used to decorate content material retrieval systems, which frequently rent supervised gaining knowledge of strategies. It explains how traditional methods of content retrieval often struggle with providing accurate and relevant search results, and how reinforcement learning offers a potential solution by continuously learning and adapting to user behavior and preferences. The abstract also highlights the benefits and challenges of using this approach, as well as the potential impact it could have on the field of information retrieval. It could permit greater scalability and advanced user experience. The proposed method is predicted to surpass existing content material retrieval structures in terms of content material accuracy and efficiency. © 2023 IEEE.

关键词： Artificial Intelligence Data Mining Information Retrieval Optimization Personalization Search Algorithms User Experience

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