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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是341-350 订阅

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Innovations in Prostate Cancer Gleason Grading: A Comprehensive Review 4

Innovations in Prostate Cancer Gleason Grading: A Comprehens...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sojitra, Harsh Patel, Pratik Piet Parul University Computer Engineering Department Gujarat Vadodara India Piet Parul University Computer Science & Engineering Department Gujarat Vadodara India

ISBN: (纸本)9798331523923

Prostate cancer is one of the most common cancers affecting men globally, and grading is crucial in treatment and survival. Structured images such as histological images that are used to determine the aggressiveness of prostate cancer and the Gleason grading system have been enhanced by ML and DL technology. However, some challenges remain though, including how to address the complexity of the histological data, how to make sure that different numbers of datasets yield similar results and how to interpret and repeat algorithms for histology. In recent years, other approaches such as transfer learning, attention mechanism, and ensemble methods seem to work relatively better but are attained with challenges like overfitting and high computational complexity. Herein the methods applied in Gleason grading classification such as datasets, ML and DL techniques and the performance measures like recall, accuracy, precision and f1-score are outlined in this review. Thus, the study provides directions for future research, the contribution and usefulness of current methods, and goals for enhancing automated Gleason grading systems. © 2025 IEEE.

关键词： Transfer learning

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Application of Big Data in user Demand Analysis of Environmental Design 1

Application of Big Data in user Demand Analysis of Environme...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Gao, Jinli College of Environmental Art and Architectural Engineering Heilongjiang University of Technology Heilongjiang Jixi China

ISBN: (纸本)9798331529246

Big data technology provides new solutions for analyzing user demands in environmental design, though challenges remain in accurately capturing these needs. This study addresses these issues by first collecting and organizing multidimensional user behavior data. It utilizes natural language processing (NLP) to preprocess text data from social media and user feedback. Clustering algorithms then group the user behavior data, while association rule mining examines the relationships between user behaviors and environmental design elements. Finally, a Long Short-Term Memory (LSTM) model predicts future trends in user demand. Experimental results show that multi-source data fusion achieves an 85% accuracy in demand recognition. The K-means clustering method records an average silhouette coefficient of 0.72, and the LSTM model attains a prediction root mean square error (RMSE) of 2.8 and a mean absolute error (MAE) of 2.1. These findings demonstrate that the LSTM model significantly enhances user demand recognition and optimizes environmental design, highlighting the effectiveness of multi-source data fusion and machine learning techniques in analyzing user demands. © 2025 IEEE.

关键词： Ecodesign

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IoT-Enabled Remote Pre-Conditioning and Predictive Thermal Management System for Electric Vehicles in Extreme Climates 3

IoT-Enabled Remote Pre-Conditioning and Predictive Thermal M...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Ritvik, B.C. Logesh, K. Nisha, P. Ganiss Roger, L. St. Joseph's Institute of Technology Department of Electrical and Electronics Engineering OMR Chennai India

ISBN: (纸本)9798331509675

Efficient thermal management is critical for ensuring optimal performance and longevity of electric vehicles (EVs) especially in extreme climates where ambient temperature fluctuations can severely affect battery performance and driver comfort. This paper presents an innovative IoT-based system for remote pre-conditioning of the EV cabin and battery as well as predictive thermal management. By utilizing IoT sensors, edge computing and machine learning, the proposed system autonomously monitors and manages the temperature of key components based on real-time environmental and vehicle. The system uses predictive maintenance algorithms to detect potential failures early, improve vehicle performance and energy efficiency and reduce the need for reactive maintenance. This research study demonstrates how this IoT-based approach can enhance the sustainability and usability of electric vehicles in extreme climates. © 2025 IEEE.

关键词： ESP32 GPS tracking IoT Technology Pre-conditioning Raspberry Pi Thermal Management

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Performance Analysis of Resource Allocation algorithms for Vehicle Platoons over 5G eV2X Communication 17

Performance Analysis of Resource Allocation Algorithms for V...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Mandal, Gulabi Roy, Anik Palit, Basabdatta Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Advanced Technology Development Center Kharagpur India National Institute of Technology Department of Electronics and Communication Engineering Rourkela India

ISBN: (纸本)9798331531195

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic transmission of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM transmission in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET + +, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon Information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-Leader Following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X IFT, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of Information (AoI), and throughput. © 2025 IEEE.

关键词： Vehicle to vehicle communications

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Personalised Travel Ratings using AI and Enhancing User Experience Through Multi-Dimensional Analytics 4

Personalised Travel Ratings using AI and Enhancing User Expe...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Poornima, S. Krishnaveni, A. Francis Raj, J. Relin Divya, R. Rajkumar, G. Vinoth Sakthidevi, I. Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Government College of Engineering Department of Mechanical Engineering Tirunelveli India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Adhiyamaan College of Engineering Department of Information Technology Hosur India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798331523923

The rapid advancements in Artificial Intelligence (AI) have enabled the development of intelligent systems for personalized travel recommendations. This study proposes a novel framework, Generative Adversarial Networks with Real-Time Analytics for Travel Experience (GAN-RATE), designed to enhance user satisfaction by integrating synthetic data generation and real-time feedback mechanisms. The GAN component addresses the challenge of sparsely rated travel attributes by generating high-quality synthetic data, while the Real- Time Analytics module evaluates user interactions and feedback to refine the recommendation process dynamically. The proposed framework considers multiple dimensions, including comfort, cleanliness, timeliness, entertainment amenities, and customer feedback, ensuring a holistic approach to travel rating prediction. Simulation analysis is conducted to compare the performance of GAN-RATE with existing algorithms. Metrics such as accuracy, mean absolute error (MAE), precision, recall, and computational efficiency are employed to evaluate system performance. Results indicate that GAN-RATE outperforms baseline models in prediction accuracy and adaptability to sparse datasets while demonstrating superior real-time response capabilities. The findings highlight the potential of GAN-RATE in improving user experience through personalized, data-driven recommendations, setting a new benchmark for AI-based travel recommendation systems. © 2025 IEEE.

关键词： Generative adversarial networks

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Revolutionizing Healthcare: The Role of AI and BigData Analytics in Disease Prevention and Management 4

Revolutionizing Healthcare: The Role of AI and BigData Analy...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gatkine, Vaishnavi Khode, Saksham N. Gourshettiwar, Palash Computer Science & Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha440001 India

ISBN: (纸本)9798331523923

Artificial Intelligence (AI) and Big Data Analytics are greatly transforming the healthcare scenario by facilitating the early detection of diseases, personalized medicine, and improved public health interventions. AI, by employing machine learning and deep learning, processes big and complex datasets, whereas Big Data detects patterns and predicts risks, enhancing resource utilization and readiness for health emergencies. Yet, the technologies are faced with a range of challenges that include data security concerns, matters of integrating many systems, and the requirement of real-time processing in ever-changing healthcare environments. Although recent technological advancements such as sophisticated deep learning algorithms and decentralized data processing have been promising solutions to some of these challenges, issues of easy integration and privacy remain. This paper suggests an objective-oriented framework to enhance the application of AI and Big Data solutions in healthcare to overcome current limitations and establish a more efficient, personalized, and predictive healthcare system. © 2025 IEEE.

关键词： Adversarial machine learning

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Analysis of Graphene-Silicon Based Two-Port THz Patch Antenna using Machine Learning algorithms 14

Analysis of Graphene-Silicon Based Two-Port THz Patch Antenn...

引用

14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Kushwah, Vivek Singh Tomar, Geetam Singh Department of Electronics & Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Rajkiya Engineering College U.P. Sonbhadra India

ISBN: (纸本)9798331531935

The enhancement of MIMO antenna performance assumes a pivotal role in addressing the escalating demands of high-capacity wireless communication systems. These techniques, in tandem, contribute to improved data rates, expanded coverage, and a more dependable wireless connectivity. This paper proposes a hybrid twin port radiator with polarization and pattern diversity features in the THz frequency domain. The design is suitable for THz-based cellular machines due to its three main contributions: (i) a rounded wave is obtained by stimulating the silicon ceramic via fan blade formed slot;(ii) the |S11| of the designed aerial is effectively predicted by the Random Forest (RF) and XGBoost technique;(iii) the radiator is tunable due to the coating of graphene over silicon dielectric;the optimized result confirms that the planned aerial operates effectively between 2.46-3.16 THz with less than 3-dB axial ratio from 2.62-2.9 THz. © 2025 IEEE.

关键词： Random forests

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Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack 4th

Analyzing Artificial Intelligence Based Intrusion Detection ...

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4th international conference on Smart IoT systems: Innovations and Computing, SSIC 2023

作者： Kurra, Sai Suchith Rayana, Likhitha Yoosuf, Mohamed Sirajudeen School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

ISBN: (纸本)9789819751990

With the increasing complexity of the cyber-attacks, traditional intrusion systems have become inadequate in effectively detecting and preventing network attacks. Modern machine learning techniques have the capability of preventing sophisticated and aggressive cyber-attacks and have emerged as a promising approach to enhance the accuracy and efficiency of the intrusion detection. This research paper provides a comprehensive analysis and evaluation of the integration of machine learning and deep learning algorithms into intrusion detection system with respect to the Distributed Denial of Service (DDoS) attack. The study begins by presenting an overview of intrusion detection systems, highlighting their importance and significance in safeguarding network infrastructure. Subsequently, it explores supervised machine learning algorithms and deep learning models, such as Random Forest tree, Support Vector Machines (SVM), ResNet and TabNet. The paper presents the pros and cons of existing IDS models with respect to real-time processing, scalability, false positive and false negative results, and IDS vulnerabilities. The above-mentioned algorithms have been trained and evaluated with CICIDS-2017 dataset. The performance metrics used to evaluate the IDS models are accuracy, precision, F1 score, training time of each model, and adaptability to evolving attack patterns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Application of Big Data Analysis algorithms in Safety Risk Assessment of Intelligent Building Decoration Construction 3

Application of Big Data Analysis Algorithms in Safety Risk A...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Gan, Jianqiang School of Architectural Engineering Taizhou Vocational & Technical College Taizhou China

ISBN: (纸本)9798331508456

In view of the problem that safety risks in the construction process of intelligent building decoration are difficult to accurately predict and respond to, this paper proposes a construction safety risk assessment method based on support vector machine (SVM). First, data is automatically collected and processed from various sources such as construction records and design drawings. On this basis, various factors in the construction process are analyzed to extract key features closely related to construction safety risk assessment. Then the algorithm is used for data mining and classification. Risk assessment is performed based on the trained SVM classification model. The average prediction accuracy of the SVM model is as high as 98.925%, which is significantly better than the rule-based method in terms of prediction time. This paper realizes the rapid processing and analysis of real-time construction data, providing decision support for engineering managers. © 2025 IEEE.

关键词： Risk assessment

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models 3

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kumar, Gaurav Devayani, G. Saraswathi, C. Melba Kani, R. Dongre, Sandeep Mallick, M. S. Mohamed School of Computer Application Lovely Professional University Phagwara India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Mar Ephraem college of Engineering and Technology Kanyakumari India Nagpur India Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331508456

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences. © 2025 IEEE.

关键词： Marketplaces

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