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Which performs better, linear scanning or the KNN searching algorithm based on 2D grids? 3

Which performs better, linear scanning or the KNN searching ...

引用

3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Ji, Tong Lin, Yifeng Yang, Yuer Liu, Hui Jinan University College of Information Science and Technology Guangzhou China Jinan University College of Cyber Security Guangzhou China The University of Hong Kong Department of Computer Science Hong Kong Hong Kong University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China

ISBN: (纸本)9798350360813

The K-nearest neighbor (KNN) algorithm is widely used in navigation, such as traffic management, driverless vehicles, and logistics planning. While it offers powerful instance-based learning, its performance can be influenced by data distribution and distance metric choice, and scalability can be an issue with large datasets. In this paper, we compare KNN with linear scanning in 2D grid-based search algorithms. KNN excels in accuracy, multidimensional feature processing, flexibility, and stepwise optimization, making it a strong choice for efficient and accurate 2D grid data search. Especially when the dimension of the grid is 200 ∗ 200 and the grade is 1, the performance of KNN is 99.70% better than linear scanning. However, we acknowledge that KNN's performance hinges on distance metric selection, and different metrics may perform differently in various applications. KNN is also sensitive to noise and outliers, which highlights the need for future enhancements and optimizations. © 2023 IEEE.

关键词： Large datasets

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IMPROVE: Intelligent Machine Learning based Portable, Reliable and Optimal VErification System for Future Vehicles

IMPROVE: Intelligent Machine Learning based Portable, Reliab...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shreyas Madhav, A.V. Mohan, A. Tyagi, Amit Kumar Vellore Institute of Technology School of Computer Science and Engineering Chennai Campus Tamilnadu Chennai600127 India Institute of Computer Science and Engineering SIMATS School of Engineering Saveetha University Department of Information Security Chennai602105 India National Institute of Fashion Technology Department of Fashion Technology New Delhi Delhi India

ISBN: (纸本)9798350348217

The technological progress over the past decade has revolutionized the transportation domain. Autonomous and semi-autonomous vehicles have now gained the global spotlight for facilitating personal transportation with minimal manual intervention. The digitization of this industry has been accompanied by significant security challenges in terms of ensuring reliable transmission and robust communication networks which are critical for the proper functioning of the smart vehicle. The CAN bus architecture responsible to establishing connectivity within the various vital components of the car's internal architecture is a prime target for intrusions. Secure connections must also be established between the vehicle and external devices such as smartphones for enhancing the travel experience. Hence a complete security intrusion detection framework for self-driving cars is of dire need. This article introduces an Intelligent Machine Learning based Portable, Reliable and Optimal VErification System (IMPROVE) for Future Vehicles that aims to provide a viable solution to resist vehicular cyberattacks both on the internal network of the vehicle and the vehicle to device network established. The proposed framework is twofold in nature- The initial module focusses on ensuring Controller Area Network (CAN) security through machine learning modelling for intrusion detection. The second module is oriented towards utilizing data analysis to detect and block malicious behaviour on networks established with external/internal devices. © 2023 IEEE.

关键词： Autonomous vehicles

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Advancements in Object Detection for Accurate and Efficient Visual Recognition Using Machine Learning 2

Advancements in Object Detection for Accurate and Efficient ...

引用

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

作者： Fernandez, F. Mary Harin Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Chennai India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9798350383867

Object detection in images and videos is a vital task in computer vision where its application goes beyond the automobile driving and surveillance, to healthcare. The paper introduces an object detection model which is specific and powerful, that is able to recognize visually with machine learning. The framework is constructed on the basis of the recent innovations in deep learning and attentive mechanisms, which not only lead to the best performance in object detection today but also guarantee the widest applicability. Using an extensive experiment which reveals the described model superiority over the current state-of-the-art algorithms by evaluating mAP indices, whereas the latter has a normal complexity. The ablation study uncovers the impact of core parameters including attention mechanisms and semi-supervised techniques, underlining their importance in the growth of detection precision. Moreover, the illustrative comparison of outcomes from detecting objects on the framework calls for attention to the capacity of the networks to objectify in different categories. Our outcome also suggest that although the proposed framework has a human-level performance in task of object detection there is still a scope to enhance their performance above the human level. Summarizing, this work concludes the conversation and can be considered in the progress of research developments of the object detection systems aimed at practical object meeting situations. © 2024 IEEE.

关键词： computer Vision Deep Learning Machine Learning Object Detection Visual Recognition

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Customer Retention Using Machine Learning 10

Customer Retention Using Machine Learning

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Das, Lipsa Salman, Rahama Sabeer, Shaista Ansari, Subuhi Kashif Aarif, Mohd Rana, Ajay Amity School of Engineering and Technology Amity University UP Greater Noida India Jazan University Department of Computer Science Saudi Arabia Global Research Network UP Noida India College of Computer Science & Information Technology Jazan University Department of Information Technology and Security Jazan Saudi Arabia College of Computer Science and Information Technology & Security Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350382471

Maintaining customer retention stands as a pivotal element in ensuring business success, given that the costs associated with acquiring new customers often surpass those linked to retaining existing ones. Recognizing the significance of retaining a loyal customer base, businesses prioritize strategies and initiatives that cultivate lasting relationships with their current clientele, understanding that sustained customer loyalty contributes substantially to long-term viability and profitability.. Machine learning techniques offer a promising approach to predicting customer behavior and developing personalized retention strategies. In this research paper, we explore the use of machine learning for customer retention, specifically focusing on predictive modeling and recommendation systems. We review recent literature on the topic, discussing different machine learning algorithms and techniques that have been applied to customer retention. We also present a case study of a company that has successfully implemented machine learning-based retention strategies, highlighting the benefits and challenges of this approach. Our findings suggest that machine learning can be an effective tool for improving customer retention, but its success depends on several factors, including data quality, model accuracy, and implementation strategy. Our conclusion involves an exploration of the consequences of our research for forthcoming studies and practical applications within business environments.. © 2023 IEEE.

关键词： customer behavior Customer retention machine learning predictive modelling

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Smart Music Recommendation System Integrating NLP and Hybrid Algorithms

Smart Music Recommendation System Integrating NLP and Hybrid...

引用

Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： S. Roshan Kumar S. S. Subashka Ramesh Harsha Vardan V Adithya Yuvan Computer Science and Engineering with Specialization in Cyber Security SRM IST Ramapuram Chennai India Department of Computer Science and Engineering SRM IST Ramapuram Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In an era where technological growth gives a massive amount of knowledge in music, intuitive and personalized music delivery forms the cornerstone for better user experience and satisfaction. The increasing reliance on voice-driven interfaces can be leveraged to create newer music recommendation systems. This paper explores the development of the Smart Music Recommendation System, which will use natural language processing (NLP), speech recognition, and hybrid algorithms to recommend music to the user based on his mood input through voice. The system leverages Google Speech Recognition API for real-time voice interaction ensuring a seamless interaction between the user and the system. The system additionally detects the user’s emotional state from voice commands, while search algorithms are employed to fetch and recommend songs. A hybrid recommendation approach combines content-based filtering for emotion-specific playlist generation for every mood for an enhanced listening experience. An exceptional feature in our system is offline download using Yt-dlp to any local device and playback processes using an automated threading mechanism. The system proposed here was tested and gave efficient performance in voice interaction, song suggestion according to mood, and coordination with external platforms for retrieving and playing songs.

关键词： Sentiment analysis Accuracy Mood Filtering Multimedia systems Speech recognition Real-time systems Internet Web sites Recommender systems

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Supply Chain Optimization in Cement Industry Using Blockchain technology

Supply Chain Optimization in Cement Industry Using Blockchai...

引用

2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Hamood, Rabeya Makhdoom, Imran Iqbal, Waseem Jamil, Tariq National University of Sciences and Technology Department of Information Security Islamabad44000 Pakistan Sultan Qaboos University Department of Electrical and Computer Engineering AlKhod Muscat123 Oman

ISBN: (纸本)9798350341072

The ubiquitous use of Internet of Things devices in supply chain management has facilitated manufacturers and other stakeholders to monitor various aspects of the product or shipment. However, conventional solutions have numerous challenges ranging from data integrity, product provenance, and trust issues to scalability problems. This research demonstrates how scalability and provenance of cement bags can be ensured through a combination of blockchain technology and the Internet of Things devices. The distributed ledger securely records each step, from acquiring raw materials to delivering the finished product. The inherent transparency of blockchain technology promotes confidence among all the stakeholders in the supply chain and makes it easier to comply with legal requirements. The security and performance analysis determines that the proposed solution is secure and efficient with minimal performance overheads. © 2023 IEEE.

关键词： Blockchain

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Exploring The Efficacy of Multi-modal LLM In Endometrial Cancer Diagnosis

Exploring The Efficacy of Multi-modal LLM In Endometrial Can...

引用

Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： B. S Sumitha Mohammed Tajuddin Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India Department of CSE (Cyber Security) Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Endometrial Carcinoma (EC) remains a significant diagnostic challenge due to its heterogeneous histopathology and clinical presentation. Recent advances in Visual Language Models (VLMs) have shown promise in medical imaging. One of the emergent technologies involves to explore large language models (LLMs) abilities with multimodal data. In this research, we find how Integration of VLMs in medical imaging analysis offers substantial benefits, includes better diagnostic accuracy, enhanced clinical decision support. This study demonstrates the efficacy of a multi-modal VLM framework for automated detection and classification of Endometrial Cancer stages, achieving 92% accuracy. Our approach integrates Magnetic resonance image (MRI) of endometrial Cancer and clinical text data to enhance diagnostic decision-making.

关键词： Visualization Technological innovation Accuracy Histopathology Magnetic resonance imaging Large language models Decision making Magnetic resonance Cancer Biomedical imaging

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Secure and Responsive Human-Robot Interaction for Dementia Patients Using LSTM and IoT 3

Secure and Responsive Human-Robot Interaction for Dementia P...

引用

3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ramya, S. Radhika, M. Roychoudri, Subramani Senthil, S. Arunachalam, G. Muthulekshmi, M. Paavai Engineering College Department of Cyber Security Tamil Nadu Namakkal India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Usha Rama College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Mathematics Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

An innovative architecture that combines Long Short-Term Memory (LSTM) networks with Internet of Things (IoT) technology is presented in this paper to assist dementia patients. It solves Human-Robot Interaction (HRI) difficulties in dementia care. It also ensures security and responsiveness in HRI to establish a secure and trustworthy environment for dementia patients. LSTM networks help the robot to recognize and to adapt based on the dementia patients' behaviors. Through real-time data gathering and exchange, IoT devices offer seamless and context-aware interactions. The system protects user privacy and sensitive data with strong encryption and authentication processes. The framework uses adaptive learning methods to improve the robot's comprehension of the user's preferences and needs, offering a personalized and responsive assistance system. Simulations and real-world situations show that the proposed technology provides safe, responsive, and personalized human-robot interactions for dementia care. By presenting a potential solution to dementia care difficulties it advances assistive technology and improves quality of life for dementia patients. © 2024 IEEE.

关键词： Human robot interaction

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Fuzzy System of IT-Project Works Priority 14

Fuzzy System of IT-Project Works Priority

引用

14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Vasylkiv, Nadia Dubchak, Lesia Karpinski, Mikolaj Turchenko, Iryna Flud, Liubomyr Nadvynychna, Tetiana West Ukrainian National University Department of Information Computer Systems and Control Ternopil Ukraine West Ukrainian National University Department of Computer Engineering Ternopil Ukraine University of the National Education Commission Department of Cyber Security Krakow Poland Ukrainian National Forestry University Department of Information Systems and Computer Modeling Lviv Ukraine West Ukrainian National University Subdepartment of Psychology and Social Work Ternopil Ukraine

ISBN: (纸本)9798350350036

A fuzzy system for setting the priority of IT project work has been developed, in which a preliminary analysis of the metrics of readiness, permission and complexity of tasks is carried out, which makes it possible to determine the priority of the project work. Based on this, the business analyst can effectively make decisions about the workload of the project team. The results of the work can be successfully applied to the effective management of the implementation of IT projects and the proactive management of project teams. © 2024 IEEE.

关键词： Project management

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Developing Real-World Applications with Algorithmic Intelligence in Reinforcement Learning for Robotics 2

Developing Real-World Applications with Algorithmic Intellig...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Bhanumathi, M. Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Chennai India Department of Computer Engineering University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering Simats Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

In the study, three leading reinforcement learning algorithms Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor - Critic (A3Cc are applied in a number of real-world robotics tasks. The focus of our analysis is on performance indicators, computational efficiencies across multiple environments as well with respect to customer satisfaction rating. The DQN demonstrated good performance, with the success rate of up to 85% in object manipulation tasks and confirmed its reliability under controlled lab conditions where it made only a 5 percent error. PPO excelled in autonomous navigation (82%) and was the most energy-efficient option, requiring just 10 hours for training and using only twelve KWH of power. Compared with A3C, the method performed to a higher level of adaptability and robustness, especially in object manipulation (90% success rate) and dynamic environments (10% error rates applicable for outdoor variable weather conditions). But A3C was also the most automatable, which required 15 hours to train and consumed an astonishing amount of electricity - 18 kWh. Regarding customer satisfaction, PPO performs the best in end-user rating on user interface;A3C shows a preference with engineers due to its advanced features and flexibility. The focus of this study is significant trade-offs between performance, efficiency and user preference in real robotics applications that advocate for individual tailoring selections as well as deployments based on task specifics nature. © 2024 IEEE.

关键词： Customer satisfaction

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