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Advanced Veterinary Clinic Management Predictive Analytics and Blockchain Innovation 3

Advanced Veterinary Clinic Management Predictive Analytics a...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sai, R. Vijay Geetha, B.G. Hariharan, A. Haripriya, A. Kameshwaran, K. Computer Science and Engineering K S Rangasamy College of Technology Tamil Nadu India

ISBN: (纸本)9798350375190

Enhancing the security, effectiveness, and transparency of healthcare data, distributed ledger technology like blockchain holds the potential to completely transform the healthcare sector. Sensitive patient information is more difficult for bad actors to bre ach thanks to the SHA-256 algorithm, which improves data security. Standardized data formats and smart contracts make it easier for disparate healthcare systems to communicate with one another. Block chain deployment in the healthcare sector is not without its challenges, though, including user uptake, scalability, and regulatory compliance. By highlighting the potential benefits and difficulties of incorporating block chain technology in healthcare management systems, this study provides significant insights into the evolving landscape of healthcare data management. © 2024 IEEE.

关键词： Blockchain

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Facial Emotion Recognition and Detection Using Convolutional Neural Networks with Low Computation Cost 2

Facial Emotion Recognition and Detection Using Convolutional...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mehrotra, Manvendra Singh, Kartikeya Pratap Singh, Youddha Beer Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350371055

People have always had the natural capacity to read emotions from facial expressions. On the other hand, creating a machine that can recognise emotions is a very difficult task. Recent developments in computer vision and machine learning have made it easier to identify emotions in images. This research suggests a convolutional neural network based approach for facial emotion recognition. There are two stages to the Facial Emotion Recognition (FER) system, and Convolutional Neural Networks (CNNs) are used in both. The model is trained during the first phase, and its accuracy is tested during the second phase. The FER model is capable of accurately identifying seven different facial emotions. Our method purposefully sacrifices accuracy % in order to improve computational efficiency and real-time prediction capabilities, whereas previous research primarily focused on accuracy without taking prediction time or processing resources into account. Our suggested approach achieved 71.61% accuracy on the FER2013 dataset, which is marginally less than the accuracy of the most advanced algorithms, which was 75.2%. On the same system, those algorithms took 93 minutes for computation training;our algorithm, however, drastically cut that time down to 58 minutes. We expect that FER's emotion recognition capabilities will be useful in a variety of domains, including robotics, the detection of lies, and the prediction of student behaviour. © 2024 IEEE.

关键词： Computational Power Convolutional Neural Network (CNN) Facial Emotion Detection

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Bone Fracture Detection and Classification using Machine Learning 2

Bone Fracture Detection and Classification using Machine Lea...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Chawhan, Lata Naik, Madhura Mohan Madhura, R. Ramya, V. Bhanushree, K.J. Poorvitha, H.R. Bangalore Institute of Technology Computer Science and Engineering Bengaluru India Information Science and Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (纸本)9798350372892

Common injuries like bone fractures need to be accurately diagnosed and classified in order to receive the right care. While conventional techniques rely on radiographic imaging, emerging developments in machine learning have indicated that this process may be automated. In this work, a convolutional neural network (CNN) model for X-ray image-based bone fracture detection and classification is presented, the model detects whether a bone is normal or fractured based on its x-ray image, and if it is, it classifies the type of fracture that occurred. The experimental results show that the suggested model is effective, with an accuracy of 90% in fracture identification and classification, exceeding existing approaches. This study advances automated fracture diagnosis, which could improve patient care and treatment outcomes and provides an efficient and user-friendly paradigm for healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Empowering Dynamic Scheduling in IoT Networks through Collaborative Policy Learning via Federated Reinforcement Techniques 2

Empowering Dynamic Scheduling in IoT Networks through Collab...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Thirunavukkarasu, M. Thaiyalnayaki, S. Jose, Naduvathezhath Nessariose Maheshwari, S. Pavithra, B. Malathi, G. Department of Computer Science & Applications S.A College of Arts & Science India Department of Computer Science Rabiammal Ahamed Maideen College for Women India Denken Solutions India Department of Information Technology Sri Sai Ranganathan Engineering College India Department of Computer science and Engineering Sri Sai Ranganathan Engineering College India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9798350383867

In the burgeoning landscape of Internet of Things (IoT) networks, efficient management of resources is paramount for ensuring optimal performance and resource utilization. Dynamic scheduling, particularly in the context of cloud-edge-terminal IoT networks, presents a significant challenge due to the diverse and dynamic nature of connected devices and their varying computational requirements. Traditional centralized approaches to scheduling may prove inadequate in such dynamic environments, necessitating the exploration of novel techniques. This project proposes a pioneering approach to address the dynamic scheduling challenges in IoT networks by leveraging collaborative policy learning through federated reinforcement techniques. The proposed framework harnesses the power of federated learning, a decentralized machine learning paradigm, to collectively train policies for dynamic scheduling tasks across distributed edge and terminal devices while preserving data privacy and security. Key components of the proposed framework include a collaborative learning architecture that orchestrates the exchange of policy updates among edge and terminal devices, enabling them to adaptively refine their scheduling policies based on local observations and feedback. Reinforcement learning serves as the underlying mechanism for policy optimization, allowing devices to learn and adapt to evolving network conditions and user demands over time. By decentralizing the learning process and leveraging the collective intelligence of edge and terminal devices, the proposed framework offers several advantages. These include improved scalability, reduced communication overhead, and enhanced resilience to network failures. Furthermore, the federated approach ensures data privacy and regulatory compliance by keeping sensitive information localized to individual devices. To evaluate the effectiveness of the proposed framework, comprehensive simulations and real-world experiments will be conducted u

关键词： Reinforcement learning

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Empowering Consumers and Sustainability: A Global Review of Right to Repair Policies

Empowering Consumers and Sustainability: A Global Review of ...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Preethish Kumar, M. Usha, B.A. Basavaraj, G.N. Mohan, B.A. Prithvi, G. Rajkumar Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Nagarjuna College of Engineering and Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375442

The 'Right to Repair' (R2R) movement has gained significant attention in recent years with the aim empowering consumers to fix their products rather than replacing them when they become faulty. This paper offers a comprehensive analysis of the impact of R2R policies on consumer rights, environmental sustainability, economic outcomes, social implications, and technological advancement in the digital era. Further, this paper discusses the role of open repair ecosystems in fostering innovation, facilitating aftermarket services, and empowering informed consumer choices. It examines the legal and regulatory frameworks surrounding R2R, including recent legislative initiatives at multiple levels. The paper concludes by advocating for R2R as a fundamental aspect of consumer rights and environmental stewardship, highlighting its potential to promote sustainability, foster innovation, and create a more equitable economy. © 2024 IEEE.

关键词： Sustainable development goals

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Sentimental Analysis on Twitter data using Deep Learning 2

Sentimental Analysis on Twitter data using Deep Learning

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Brindha, V. Shanthi, S. Bhavishya, C. Durgashree, V. Devi, K. Nirmala Jaganathan, P. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of MCA Dindigul India

ISBN: (纸本)9798350349221

In today's interconnected world, social media platforms have enabled individuals to easily communicate and share their emotions with people worldwide. In the current context, some individuals exhibit a higher degree of extroversion on social media compared to their offline interactions. However, when they encounter difficulties or situations requiring support, they may not find the help they need because they tend to express their feelings on social media rather than seeking assistance from those close to them. Consequently, if these individuals are forewarned and equipped with effective strategies, it could potentially reduce the stress and mental health challenges they experience. As a result of the escalating global prevalence of these issues, numerous researchers have been drawn to this topic, focusing on the matter and proposing practical solutions. The study employs state-of-the-art NLP techniques, including tokenization, word embedding, and deep learning. © 2024 IEEE.

关键词： Social networking (online)

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Harnessing Bio-Inspired Optimization and Swarm Intelligence for Energy-Aware TinyML in IoT 7

Harnessing Bio-Inspired Optimization and Swarm Intelligence ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kalyanakumar, P. Pandian, S. Srinivasa Boopalan, S. Jesintha, D. Kani Krishnan, R. Santhana Muthu, A. Essaki Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

This research investigates the integration of bio-inspired optimization and swarm intelligence principles with TinyML for the development of energy-aware Internet of Things (IoT) devices. A novel model algorithm, termed "BioSwarmML,"is introduced and evaluated against existing algorithms through comprehensive simulation analyses employing suitable metrics. The proposed framework aims to enhance energy efficiency in IoT applications by leveraging the collective intelligence derived from bio-inspired optimization and swarm behaviors. The "BioSwarmML"algorithm is designed to draw inspiration from natural processes, incorporating bio-inspired optimization techniques such as genetic algorithms, simulated annealing, and evolutionary strategies. Concurrently, swarm intelligence principles are integrated to emulate decentralized and self-organizing behaviors observed in biological systems. This amalgamation aims to optimize the energy consumption of TinyML models on IoT devices, facilitating sustainable and adaptive learning processes. Simulation analyses involve a comparative study with established algorithms, evaluating BioSwarmML based on metrics such as energy consumption, accuracy, and latency. The results demonstrate the efficacy of the proposed algorithm in achieving superior energy efficiency while maintaining competitive performance in terms of accuracy and responsiveness. The comparison sheds light on the advantages of BioSwarmML in energy-aware TinyML applications, showcasing its potential for widespread adoption in IoT ecosystems. This research contributes to the advancement of energy-efficient IoT systems by introducing a novel algorithmic paradigm that aligns with international journal standards. The proposed "BioSwarmML"model showcases a promising avenue for enhancing sustainability in TinyML-driven IoT applications, offering a valuable addition to the existing body of knowledge in the field. © 2024 IEEE.

关键词： Internet of things

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Enhanced Resource Allocation Strategy using Cox Regression for Wormhole Attack Mitigation in Routing Protocols 5

Enhanced Resource Allocation Strategy using Cox Regression f...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Praveenkumar, G.D. Ilango, M. Mohanasathiya, K.S. Naveen, N. Rakkimuthu, P. Kongu Engineering College-Erode Department of Computer Technology -UG India Government Arts and Science College Gudalur Department of Computer Science Tamilnadu The Nilgiris India VET Institute of Arts and Science Co-Education College- Erode Department of Computer Science India Excel Engineering college Department of Information Technology Tamilnadu India

ISBN: (纸本)9798350367171

MANETs, as self-configuring networks lacking a fixed infrastructure, are exceptionally vulnerable to a multitude of security threats. One such severe threat is the Wormhole Attack, where malicious nodes create a virtual link between two distant points in the network, creating a shortcut for communication. This deceptive behavior disrupts the routing protocols and leads to severe consequences, including data manipulation and network partitioning. The Wormhole Attack has had a significant impact on MANETs. This study stimulates COX Regression techniques that are used to calculate the regression coefficient for the association between mobile nodes to perform routing in the MANETs. Based on the calculation of residual energy, the delay and bandwidth are identified as optimal. Additionally, various mitigation techniques are proposed and evaluated to improve the network's security in the face of this sophisticated attack. According to the computation of residual energy, delay and bandwidth is detected as optimal one. Additionally, this study proposes and evaluates different mitigation techniques to enhance the network's security against this sophisticated attack. The proposed methods findings highlight the importance of proactive measures and vigilance in safeguarding MANETs from Wormhole Attacks. This helps to improve the rate of packet delivery with reduced latency and the lifetime of data transmission within the network. © 2024 IEEE.

关键词： Bandwidth

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Voice-Based Virtual Assistant for Windows (Ziva - AI Companion) 5

Voice-Based Virtual Assistant for Windows (Ziva - AI Compani...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sunil Patel, Shubham Sonam Srivastav, Vaishnav Galgotias College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

ZIVA is to create a Python-based Desktop Assistant, leveraging Python's versatility and simplicity. Python is a widely used programming language in the field of Dialog Flow and AI, known for its adaptability and longstanding reputation. This Project is also based on the NLP. The primary goal of this project, named Desktop Assistant (ZIVA), is to enable users to issue commands to their local machines using voice recognition, eliminating the need for manual typing. Virtual assistants such as Siri for iOS and Cortana for Windows serve as inspiration for ZIVA. aiming to streamline the process of instructing the computer to perform various operations. ZIVA incorporates intelligent voice recognition to process user input in the form of voice commands and provide outputs such as actions to be executed or search results. Voice commands are the only way that users can engage with the assistant. ZIVA's intelligence includes the ability to store detected voice commands and match them with predefined default commands. As a Desktop Assistant, ZIVA helps with everyday tasks such as engaging in general human conversation, conducting Google searches, opening websites like YouTube, Wikipedia, and Stack Overflow, searching for and playing music, telling the time, interacting with the user, and shutting down the system. User statements and commands are analyzed using machine learning techniques to provide optimal solutions. © 2024 IEEE.

关键词： Artificial intelligence Learning robotics NLP (Natural Language Processing) Advanced engineering Virtual assistance

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Research on automatic driving scene object detection algorithm based on improved YOLOv8 4

Research on automatic driving scene object detection algorit...

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4th International Conference on Testing technology and Automation engineering, TTAE 2024

作者： Liu, Yunxiang Ren, Xiaoya Zhu, Jianlin College of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

ISBN: (数字)9781510686656

ISBN: (纸本)9781510686649

In response to the challenges associated with detecting occluded and small targets in automatic driving scenarios, as well as the issues of low detection accuracy and a high miss rate caused by complex background interference, an enhanced road target detection algorithm based on YOLOv8 is proposed. A plural lightweight convolutional module (PLConv) is devised to replace the C2f module with the PL-C3 module, thereby reducing network parameters and enhancing the network's feature extraction capabilities. Moreover, a P2 small target detection head is integrated at the model's apex, facilitating improved extraction of shallow features and enhancing the model's performance in detecting small targets. Experimental results demonstrate that the enhanced model has seen increases of 5.8%, 5.2%, and 3.5% in mAP50, mAP50-90, and Recall respectively, thus better aligning with the demands of object detection tasks in automatic driving scenarios. © 2024 SPIE.

关键词： Automatic target recognition

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