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Futuristics Technologies for Reduction of Plastic Waste 13

Futuristics Technologies for Reduction of Plastic Waste

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Dhiman, Riya Sharma, Ashish Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Electrical Engineering Mohali India

ISBN: (纸本)9798350380583

Plastic plays a vital role in daily life most of the food items, Baggies, vegetables are packed in plastic material. As plastic is non-biodegradable there are harming effect on human beings and animals. Due to these global warming also increases. Although 80% Fishes, 88% Vertebrates, 56% Mammals and 52% Reptiles are affected due to plastic waste. In this work with the various techniques proposed like Eco-bricks, Furniture, Replace Plastic By Bamboo and also Doormats are made through the Plastic Waste. It is seen that by using above techniques significantly reduce the harmful effect of plastic and there is improvement in life of human beings and animals also the alternative techniques to reuse the plastic and to replace plastic are proposed with which there is a increase in the earning of poor peoples. Above techniques are helpful to decrease the employment and also by using these techniques more than 60% of plastic reuse, with this the temperature is also decreased. © 2024 IEEE.

关键词： Invertebrates

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Towards Robust mmWave-based Human Activity Recognition using Large Simulated Dataset for Model Pretraining

Towards Robust mmWave-based Human Activity Recognition using...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Joshi, Vinay Xu, Shengkai Cao, Qiming Zhu, Yi Wang, Pu Xue, Hongfei Unc Charlotte Dept. of Computer Science Charlotte United States Purdue University Electrical and Computer Engineering West Lafayette United States Wayne State University Dept. of Computer Science Detroit United States

ISBN: (纸本)9798350362480

Human activity recognition (HAR) is crucial for real-world applications such as healthcare, surveillance, and smart homes. Among sensing technologies, millimeter wave (mmWave) sensors stand out due to their contactless nature, high sensitivity, and ability to operate in low-light environments while preserving privacy. However, the scarcity of mmWave sensing data limits the generalizability of mmWave-based HAR systems. To address this, we propose mmAP, a data augmentation and pretraining framework that synthesizes a large mmWave dataset using human mesh data, followed by pretraining a robust and general mmWave heatmap encoder using a multi-modal masked autoencoder framework using the synthesized data. We enhance the model's robustness with heatmap-specific data perturbations and perform task-specific fine-tuning on a small real-world dataset. The experiment results over the baseline demonstrate the effectiveness of the proposed mmAP framework. © 2024 IEEE.

关键词： Large datasets

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Towards Fast Rates for Federated and Multi-Task Reinforcement Learning 63

Towards Fast Rates for Federated and Multi-Task Reinforcemen...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhu, Feng Heath, Robert W. Mitra, Aritra North Carolina State University Dept. of Electrical and Computer Engineering United States University of California Dept. of Electrical and Computer Engineering San Diego United States

ISBN: (纸本)9798350316339

We consider a setting involving N agents, where each agent interacts with an environment modeled as a Markov Decision Process (MDP). The agents' MDPs differ in their reward functions, capturing heterogeneous objectives/tasks. The collective goal of the agents is to communicate intermittently via a central server to find a policy that maximizes the average of long-term cumulative rewards across environments. The limited existing work on this topic either only provide asymptotic rates, or generate biased policies, or fail to establish any benefits of collaboration. In response, we propose Fast-FedPG - a novel federated policy gradient algorithm with a carefully designed bias-correction mechanism. Under a gradient-domination condition, we prove that our algorithm guarantees (i) fast linear convergence with exact gradients, and (ii) sublinear rates that enjoy a linear speedup w.r.t. the number of agents with noisy, truncated policy gradients. Notably, in each case, the convergence is to a globally optimal policy with no heterogeneity-induced bias. In the absence of gradient-domination, we establish convergence to a first-order stationary point at a rate that continues to benefit from collaboration. © 2024 IEEE.

关键词： Federated learning

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A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning 3

A Comprehensive Analysis on Skin Cancer Classification Using...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Renu, Israt Zerin Haque, Md. Momenul Paul, Subrata Kumer Mou, Mahmuda Shirin Rahman, Md Nahid Gupta, Sudipta Sen Paul, Rakhi Rani Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Qadirabad Dept. of Computer Science and Engineering Dayarampur Natore6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Skin cancer, a prevalent and potentially life-threatening disease, necessitates accurate and timely diagnosis for effective treatment. In addressing this critical healthcare challenge, this research proposes a novel approach to skin cancer classification. The study identifies the need for enhanced diagnostic accuracy and introduces a solution by integrating the efficiency of transfer learning, specifically utilizing the EfficientNet V2 architecture. The proposed methodology leverages pre-trained convolutional neural networks (CNNs) to extract high-level features from dermatological images, which are subsequently input into a sophisticated classifier, forming a robust framework for precise skin cancer classification. The research employs a diverse dataset comprising 3297 images sourced from the publicly available Skin Cancer ISIC archive dataset, ensuring comprehensive coverage of benign and malignant skin lesions. During the training phase, the proposed EfficientNet V2 transfer learning approach outperforms conventional methods, achieving an impressive accuracy of 84%, surpassing both Inception V3 (82%) and a generic CNN model (81%). Interpretability analysis is conducted to elucidate the decision-making process of the EfficientNet V2 model, providing insights into the key features influencing its predictions. This transparency is crucial for building trust in the model's recommendations and facilitating its seamless integration into clinical workflows. The EfficientNet V2 transfer learning approach emerges as a promising and effective solution for advancing skin cancer classification systems, contributing significantly to the ongoing efforts to improve diagnostic accuracy in dermatology. © 2024 IEEE.

关键词： Decision making

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Field Demonstration of Blockchain-based Security for a Solar Farm

Field Demonstration of Blockchain-based Security for a Solar...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ahn, BoHyun Nakka, Kalyan Handke, Nathanial R. Reyna, Trevor J. Kim, Taesic Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science KingsvilleTX78363 United States Texas A&m University Dept. of Computer Science and Engineering College StationTX77843 United States University of Missouri-Columbia Dept. of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798350376067

This paper introduces a Blockchain (BC)-based security model for a solar farm, providing security functions such as firmware patching management, role-based access control, public key infrastructure, and man-in-the-middle attacks detection (MITM), malware file detection. In particular, this paper provides detailed field-testing methods at a solar fam to demonstrate the feasibility and effectiveness of cyber-attack detection methods. Practical cyberattacks targeting solar farms are designed and conducted. It is demonstrated that the proposed BC-based system proactively detects MITM attacks, firmware modification attack, and malware attack, while ensuring the continuous operation of the solar farm during attack events. © 2024 IEEE.

关键词： Cyber attacks

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Towards the Compilation of Digital Twin Models for Service-Oriented Systems 34

Towards the Compilation of Digital Twin Models for Service-O...

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34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Jiang, Hao Kontogiannis, Kostas Western University Computer Science Dept. LondonON Canada York University Dept. of Electrical Eng. & Computer Science TorontoON Canada

ISBN: (纸本)9798331504830

Service-oriented architecture (SOA) serves as a foundational technology for complex IT systems, such as e-commerce platforms, banking solutions, public-sector applications, and gaming systems. As these systems become increasingly intricate, the need for models that enable software engineers to simulate component interactions early in the software lifecycle becomes crucial. This approach helps identify potential issues during the design phase. The goal of this research is to apply digital twin model principles to simulate component transactions, message exchanges, and ultimately evaluate whether a given design meets the goals and requirements of stakeholders. In this paper, we outline ongoing work towards a prototype framework that uses sequence diagrams and activity diagrams to build digital twin models of service-oriented systems. The framework aims to ensure that any design changes are reflected in the digital twin model early in the software lifecycle, potentially uncovering non-compliant or unintended interactions. © 2024 IEEE.

关键词： Service oriented architecture (SOA)

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

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2024 International Conference on electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

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Elastic Blockchain Security Solution for Edge Computing Supporting Mobile IoT Devices

Elastic Blockchain Security Solution for Edge Computing Supp...

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2024 International Conference on electrical and computer engineering Researches, ICECER 2024

作者： Almubark, Hameed Jedidi, Ahmed Al-Raweshidy, Hamed S. Brunel University London Dept. of Computer Science United Kingdom Ahlia University Dept. of Computer Engineering Manama Bahrain Brunel University of London Dept. of Electronic and Electrical Engineering United Kingdom

ISBN: (纸本)9798331539733

When integrated, blockchain technology and cloud computing (EC) may improve security and protect data. However, when data is sent to EC, the Internet of Things (IoT) device layer cannot guarantee data security. As a result, in the IoT ecosystem, end users and IoT devices are susceptible to assaults. Additionally, as the number of IoT devices rises and generates a large quantity of data, especially in mobile IoT devices, security concerns rise dramatically. High security and privacy standards are necessary for industrial Internet of Things (IIoT) applications in order to safeguard data and guarantee that IoT devices may seamlessly switch to another network. By utilising its decentralised structure, consensus process, and encryption features, blockchain technology can offer a solution. However, because IIoT devices have limited resources, the encryption procedure might be complicated. Actually, in order to secure communication between IIoT devices and EC, this research suggests a new security mechanism built on blockchain technology. Additionally, two security tiers will be used by the Elastic Blockchain Security Solution (EBC) to create a dynamic system for mobile IIoT devices. To evaluate the EBC solution's effectiveness, scalability, and performance, as well as its consensus method and encryption capabilities, it was put into practice and tested in a number of scenarios © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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A Case for Speculative Address Translation with Rapid Validation for GPUs 57

A Case for Speculative Address Translation with Rapid Valida...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Park, Junhyeok Kwon, Osang Lee, Yongho Kim, Seongwook Byeon, Gwangeun Yoon, Jihun Nair, Prashant J. Hong, Seokin Sungkyunkwan University Dept. of Electrical and Computer Engineering Korea Republic of Sungkyunkwan University Dept. of Semiconductor Convergence Engineering Korea Republic of University of British Columbia Dept. of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350350579

A unified address space is vital for heterogeneous systems as it enables efficient data sharing between CPUs and GPUs. However, GPU address translation faces challenges due to high TLB pressure, particularly with irregular and memory-intensive applications. Compared to an ideal scenario, we observe that address translation overheads cause a slowdown of up to 34.5% in modern heterogeneous systems. This paper introduces Avatar, a novel framework to accelerate address translation in GPUs. Avatar comprises two key components: Contiguity-Aware Speculative Translation (CAST) and In-Cache Validation (CAVA) mechanisms. Avatar identifies the potential for predicting virtual-to-physical address mapping by monitoring contiguous pages that lie in both virtual and physical address spaces. Leveraging this insight, CAST speculatively translates virtual addresses into physical addresses. This speculative address translation enables immediate data fetching into GPUs while addressing translation occurs in the background, reducing TLB-miss overhead. Unfortunately, modern GPUs lack support for speculative execution, which limits CAST's performance gain. Data fetched from speculated physical addresses is unusable until validation. CAVA addresses this limitation by quickly validating speculated physical addresses. To this end, CAVA embeds page mapping information into each 32B sector of 128B cache lines. Thus, CAVA enables fetching a sector block from memory for a speculated address and rapidly validating the speculative translation using the embedded mapping information. Our experiments show that Avatar achieves a 90.3% (high) speculation accuracy and improves GPU performance by 37.2% (on average). © 2024 IEEE.

关键词： Virtual addresses

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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