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Paste-and-Cut: Collective Image Localization and Classification for Real-Time Multi-Camera Object Detection 14

Paste-and-Cut: Collective Image Localization and Classificat...

引用

14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Kang, Young Eun Kang, Woosung Lee, Taehun Chwa, Hoon Sung Dgist Electronical Engineering and Computer Science Daegu Korea Republic of

ISBN: (纸本)9798350313277

In recent years, object detection has emerged as a crucial task in various real-world applications, including security surveillance, autonomous vehicles, and robotics. However, traditional object detection models face numerous challenges, such as inefficient image processing, inadequate resource utilization, and a failure to consider the different criticality of input images, making it difficult to apply these models for timely inferences in practical applications. To overcome these challenges, this paper proposes a novel object detection framework, called Paste-and-Cut, that utilizes two techniques, image merging (paste) and RoI patching (cut), to optimize resource utilization and improve object detection performance. Additionally, our approach incorporates a dynamic merge size and canvas size decision mechanism to adapt to varying object detection environments. Experimental results obtained from experiments conducted with the MOT dataset demonstrate the effectiveness of our approach in achieving real-time object detection with improved detection accuracy and without generating any deadline miss. As such, Paste-and-Cut provides a promising solution for efficient and accurate real-time object detection in multi-camera scenarios. © 2023 IEEE.

关键词： Deep neural networks Object detection Real-time systems

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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A Terse Review of Heart Diseases Estimation Using Machine Learning Practices

A Terse Review of Heart Diseases Estimation Using Machine Le...

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Pawar, Rahul Dash, Amit Kumar Mittal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

Heart diseases, a primary cause of global fatalities, represent a growing social matter on a worldwide scale. The integration of machine learning with advancing technology has greatly benefited the medical field, resulting in the provision of seamless solutions characterized by high accuracy, consistency and toughness. These advancements help the patient society through early detection and reduced medical care costs. They also offer the medical fraternity capable, accessible, precise and consistent prediction systems. This paper provides a comprehensive enquiry of machine learning algorithms engaged for the prediction and classification of several cardiovascular diseases, we explore several modalities, including physiological signals, heart sounds and electronic health records, to achieve effective heart disease recognition. We also provide insights into popular machine learning systems, hybrid systems and fuzzy systems in this context. It is noted in this review that Support Vector Machines (SVM) have gained popularity and widespread usage, with neural networks and ensemble techniques following closely behind in terms of their application. All these approaches are compared based on accuracy metrics. © 2024 IEEE.

关键词： Hybrid systems

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Navigation On Mobile Computing Devices with A Walking Using Object-Oriented Techniques to Communicate Stick 3

Navigation On Mobile Computing Devices with A Walking Using ...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Ramesh Prabhakaran, R. Guhan, P. Raj, E George Dharma Prakash Karthika, K. Angel Maanu, P. Mizoram University Department of Computer Engineering Mizoram India Panimalar Engineering College Department of Electrical and Electronical Engineering Chennai India Bharathidasan University School of Computer Science and Engineering Trichy India Karpagam College of Engineering Department of Information Technology Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331528201

Moving around in their surroundings is challenging for elderly people and people with visual impairments. The majority of functional sticks in use today are not intelligent and lack an innate ability to recognize obstacles. In an effort to address this issue, mobility and orientation specialists who assist the blind by teaching them to move freely and safely using their remaining senses are brought in. They can navigate around obstacles with the aid of guidance dogs as well. These techniques have drawbacks, such as the dogs' inability to comprehend complicated instructions, the high cost of maintaining these trained dogs, and the need to hire an expert. In light of the aforementioned difficulties, the "Smart walking stick"has become a potential answer. The "Mobile based walking stick"is a useful mobility aid with the capacity to call a relative who can help in the event of an accident, detect obstacles above ground level and pits using ultrasonic sensors, recognize when a collision has occurred, and call the user's mobile phone if it has been misplaced to share the location's GPS for use with GSM connectivity and to share with the user's mention contacts. With some visually disabled people as test subjects, a test case was run, and 95% of the time it was successful. © 2024 IEEE.

关键词： Ultrasonic sensors

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Investigating Machine Learning (ML) in Augmented and Virtual Reality: A Comprehensive Analysis in Prawn Farming

Investigating Machine Learning (ML) in Augmented and Virtual...

引用

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Painuly, Vaibhavi Kavitha, R. Ravela, Ratna Raju Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India Department of Electronical & Electronics Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

Commercial lakes ought to be kept up well in arrange to meet the expanding request for prawns universally and create at their best. This think about addresses the challenge by combining present day sensor innovation, expanded reality interfacing, and machine learning calculations. The plan employments state-of-the-art sensors to ceaselessly screen water quality, empowering proactive decision-making. Machine learning calculations analyze collected information to expect lake conditions, giving agriculturists with a see of potential future occasions. Increased reality representation encourages way better information collection and real-time decision-making through intuitively interfaces. This all-encompassing approach progresses efficiency, maintainability, and effectiveness within the prawn cultivating industry by revolutionizing current hones. © 2024 IEEE.

关键词： Adversarial machine learning

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An Extensive Review of Optimized Energy Saving Approaches for Green Cloud Computing (GCC)

An Extensive Review of Optimized Energy Saving Approaches fo...

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Mehta, Deepak Tripathi, Amit Kumar Moazzam, Javed Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

The proliferation because of cloud computing has resulted in inefficient power usage in the areas of data transmission, storing, and handling. This activity is environmentally detrimental due to the release of emissions of carbon. Hence, the implementation of green IT is necessary in order to preserve the surroundings. The environmentally conscious computing in the cloud (GCC) method is an element of green IT that tries to minimize the ecological impact of information centers by reducing the amount of energy they use. The GCC, or Global Climate Change, is a vast and captivating area for scientific investigation. Several studies have been conducted to enhance the utilization of computing resources in line with the GCC vision. The given paper concentrates on diverse facets, encompassing approaches for optimizing software, hardware, and network systems. Also, this paper summarizes and categorizes various GCC approaches. In order to make meaningful comparisons between GCC methods, it is helpful to first identify the central implementation strategies and the problems associated with each. © 2023 IEEE.

关键词： and automation Cloud computing energy conservation energy planning sustainable computing

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Applying DMLP and HMM in Speech Identification Platforms

Applying DMLP and HMM in Speech Identification Platforms

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Bargavi, Manju Singhal, Rishi Moazzam, Javed Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

Attempting to deploy high-precision speech recognition algorithms on mobile and embedded devices without doing a thorough evaluation of their computational resource requirements is impractical. This study utilizes techniques to minimize the computational resources required for an optimized voice recognition system implemented on mobile devices. A cutting-edge voice recognition technique, known as Dynamic Multi-Layer Perceptron (DMLP), has been created. This method is capable of functioning in real-time on a contemporary mobile device. When evaluated on the same dataset, traditional hidden Markov models (HMM) performed somewhat better than our method, but this came at the cost of significantly longer processing time. The provided Dynamic Multi-layer Perceptron (DMLP) achieves a remarkable accuracy of 96.94% and outperforms other approaches in terms of speed. © 2023 IEEE.

关键词： and hidden Markov models Dynamic multilayer perceptron Fourier transforms linear predictive cepstral coefficients Mel-frequency cepstral coefficients

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Few-Shot Name Entity Recognition on StackOverflow 9

Few-Shot Name Entity Recognition on StackOverflow

引用

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Chen, Xinwei Li, Kun Song, Tianyou Guo, Jiangjian University of Illinois at Urbana Champaign Department of Electronical and Computer Engineering UrbanaIL United States University of Illinois at Urbana Champaign Department of Computer Science UrbanaIL United States Columbia University Department of Computer Science New York United States University of California San Diego Department of Computer Science and Engineering La Jolla United States

ISBN: (纸本)9798350376548

StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us. We address this gap by proposing RoBERTa+MAML, a few-shot named entity recognition (NER) method leveraging meta-learning. Our approach, evaluated on the StackOverflow NER corpus (27 entity types), achieves a 5% F1 score improvement over the baseline. We improved the results further domain-specific phrase processing enhance results. © 2024 IEEE.

关键词： Zero-shot learning

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Mix of Experts Language Model for Named Entity Recognition 6

Mix of Experts Language Model for Named Entity Recognition

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

ISBN: (纸本)9798350353174

Named Entity Recognition (NER) is an essential steppingstone in the field of natural language processing. Although promising performance has been achieved by various distantly supervised models, we argue that distant supervision inevitably introduces incomplete and noisy annotations, which may mislead the model training process. To address this issue, we propose a robust NER model named BOND-MoE based on Mixture of Experts (MoE). Instead of relying on a single model for NER prediction, multiple models are trained and ensembled under the Expectation-Maximization (EM) framework, so that noisy supervision can be dramatically alleviated. In addition, we introduce a fair assignment module to balance the document-model assignment process. Extensive experiments on real-world datasets show that the proposed method achieves state-of-the-art performance compared with other distantly supervised NER. © 2024 IEEE.

关键词： component Mix of expert Named Entity Recognition NLP

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VLC positioning in low data rate for V2V communication 2024

VLC positioning in low data rate for V2V communication

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12th Euro-American Conference on Telematics and Information Systems, EATIS 2024

作者： Giraldo, Luis Miguel Soler, Joaquin Perez Botella-Mascarell, Carmen Juan, Vicent Girbes Varea, Sandra Roger Torres, Julio Martos Olcina, Raimundo Garcia Electronical Engineering Department Universitat de València Burjassot Valencia46100 Spain Computer Science Department Informatics Department Universitat de València Burjassot Valencia46100 Spain

ISBN: (纸本)9798400717338

The creation and implementation of a system that bridges visible light communication (VLC) with selected identifiers, envisioned as informational beacons, are outlined. This system is adept at controlling LED panels and orchestrating communication between LED lights and the receiving unit, laying the groundwork for vehicle-to-vehicle (V2V) communication systems tailored for precise positioning. The design and assessment of test-bed requirements cater to the anticipation of future V2V optical wireless communication scenarios. Through analysis, it is demonstrated that commercial outdoor lighting can be effectively utilized for V2V positioning, capable of operating under daylight with a data transmission rate of 100 Kbps. Crucially, the system's ability to accurately detect positioning beacons facilitates the localization of vehicles within designated areas of interest, paving the way for the advancement of V2V communication technologies. This comprehensive approach not only validates the feasibility of using VLC for vehicle positioning but also sets a precedent for future developments in V2V communication systems. © 2024 Owner/Author.

关键词： Optical communication

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