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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86325 条记录，以下是221-230 订阅

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Facial Age Estimation with Deep Convolutional Neural Network Architecture

Facial Age Estimation with Deep Convolutional Neural Network...

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2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Tchin, Jalaludin Ar Raniry Wiliam Winata, Alvin Prasetyo, Simeon Yuda Purwanto, Eko Setyo School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Facial age estimation is a complex and essential task with applications in biometrics, healthcare, and personalized services. This study explores the use of pre-trained deep convolutional neural network (CNN) architectures, including Xception, Inception, MobileNet, ResNet, and Inception ResNet, to predict age from facial images. These models leverage their hierarchical feature extraction capabilities to capture age-relevant patterns accurately. The evaluation was conducted using key regression metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the tested models, Xception demonstrated the best performance, achieving a MAE of 2.95, MSE of 48.59, and RMSE of 6.97, making it the most accurate and reliable architecture in this study. The findings underscore the effectiveness of pre-trained CNNs in handling the complexities of facial age estimation and emphasize the importance of selecting an appropriate model architecture and optimization techniques. While Xception showed strong accuracy, challenges such as overfitting and dataset bias were partially mitigated through fine-tuning. Future research could focus on incorporating multi-modal data and optimizing these models for deployment on resource-constrained devices. This study provides a robust foundation for advancing facial age estimation systems in both research and practical applications. © 2025 ieee.

关键词： Convolutional neural networks

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 ieee.

关键词： Recommender systems

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Improvement and Performance of data Transmission Algorithms in Computer Communications 14

Improvement and Performance of Data Transmission Algorithms ...

引用

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

作者： Liu, Yi Liu, Hao Cheng, Yun Cheng, Siqiang Information School Hunan University of Humanities Science and Technology Loudi417000 China

ISBN: (纸本)9798331531935

In response to the current problems of low data transmission efficiency, high latency, and insufficient stability in computer communications, this paper studies a comprehensive optimized data transmission algorithm, which aims to improve network resource utilization efficiency and transmission performance and reduce the frequency of congestion by dynamically adjusting the transmission rate, adaptive congestion control and queue management optimization. Firstly, a dynamic rate adjustment mechanism based on real-time network status detection is designed to monitor the network bandwidth, latency, and packet loss rate in real time to dynamically adjust the data packet transmission rate. Secondly, an adaptive congestion control strategy is applied to reduce the impact of congestion on data transmission by monitoring the network congestion status in real time and adjusting it according to the changing trend. Finally, queue management and priority scheduling algorithms are used to reasonably arrange the transmission order of data packets, reduce queuing delays, and improve the transmission efficiency of high-priority data packets. In terms of latency and throughput, as latency increases, throughput gradually decreases. Taking the bandwidth of 100Mbps as an example, when the network latency is 20ms, the throughput is 92.15Mbps, and when the latency is 100ms, the throughput drops to 74.11Mbps. The optimized data transmission algorithm proposed in this paper significantly improves the efficiency and stability of network transmission by dynamically adapting to network conditions. In future research, the application of this algorithm can be further expanded, and its adaptability and robustness in more complex network environments and high-load applications can be enhanced to improve the reliability of data transmission. © 2025 ieee.

关键词： Bandwidth

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H2O Guard: Predicting Water Potability with Machine Learning and Blockchain Integration 3

H2O Guard: Predicting Water Potability with Machine Learning...

引用

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

作者： Nair, Rahul Aalim, Ansab Manhaas, Ishaan Rastogi, Kshitij Kalpana, A.V. Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798331509675

Access to clean drinking water is critical to public health and well - being. Water compatibility refers to the safety of drinking water for human consumption and includes various physical, chemical and biological parameters. Contaminated water can cause serious health problems, particularly among vulnerable populations due to water-borne diseases. Traditionally, water quality assessment relies on time - consuming laboratory tests that can delay responses to contamination. The H2OGuard project presents a novel approach: It uses deep learning and machine learning algorithms to predict water sustainability based on key environmental factors such as pH, turbidity and chemical contaminants. With these advanced methods, we aim to improve the efficiency and accuracy of water quality testing. In addition, the integration of blockchain technology is intended to ensure secure and immutable storage of water quality data and promote integrity and transparency. This paper discusses the methods of the H2OGuard project, evaluates the performance of various predictive models, and highlights the critical importance of blockchain in securing data. With this innovative framework, we aim to enable more efficient and timely assessment of drinking water safety, ultimately contributing to improved public health outcomes. © 2025 ieee.

关键词： Electronic health record

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Expanding the PDE Cache by Exploiting Underutilized TLB Entries for data-intensive Workloads

Expanding the PDE Cache by Exploiting Underutilized TLB Entr...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Hyeong Jin Kim, Woo Hyun Han, Tae Hee Sungkyunkwan University Department of Semiconductor Convergence Engineering Suwon Korea Republic of Sungkyunkwan University Department of Semiconductor Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798331510756

data-intensive applications access large address spaces and exhibit low locality when accessing virtual addresses (VA). The level 2 translation lookaside buffer (L2 TLB) misses trigger page table walks (PTWs), and the PTWs become a bottleneck, requiring up to five memory accesses for address translation. Among four page caches used to reduce memory accesses during PTWs, the page directory entry cache (PDE cache) experiences the highest number of miss counts. In addition, the L2 TLB indicates an underutilized characteristic with a low reuse count of L2 TLB entries. The study proposes an architecture that stores PDE cache entries in the L2 TLB, leveraging the underutilized property of the L2 TLB and the strain on the PDE cache. The expansion of the PDE cache storage space results in a diminution of memory accesses during PTWs, leading to enhanced performance in data-intensive applications. Even with the reduced L2 TLB capacity, the impact on performance degradation is minimal since the reduction affects the space occupied by underutilized entries. The proposed architecture demonstrates a 76.77% decrease in average memory access counts during PTWs and a 6.41% improvement in average instructions per cycle (IPC). © 2025 ieee.

关键词： Virtual addresses

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Deploying Deep Learning to Improve the Performance of MIMO-OFDM systems Using LDPC Channel Coding in V2X Communications 19

Deploying Deep Learning to Improve the Performance of MIMO-O...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Nga, Nguyen Thu Duc, Nguyen Dinh Tien, Trinh Manh WICOM Lab School of Electrical and Electronic Engineering SEEE Hanoi University of Science and Technology HUST Hanoi Viet Nam

ISBN: (纸本)9798331507817

This paper focuses on the application of Deep Learning technology to improve the performance of V2X (Vehicle-to-Everything) communication systems. Of the Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over a traffic environment, supported by Dedicated Short-Range Communication (DSRC) technology, we deploy channel coding algorithm of Low-density parity-check code (LDPC) for correcting error derived from the transmission media. Artificial Neural Network (ANN), an algorithm that can interpret raw data, is used to simulate real-world data. We apply ANN with layers as a mathematical model to train the path gain parameter, which thereafter examined the system performance BER of 5G channel coding MIMO-OFDM system over Rayleigh fading channel. We evaluate the BER and the throughput at different vehicle speeds of 60 km/h or 120 km/h as well as training process of 50 data samples or 250 data samples in scenarios of 2×2 or 4×4 MIMO antennas deployed DSRC technique. From the simulation results, training model for optimizing path gain has yielded positive results in terms of system performance and throughput, even at lower 50 data sample training. It can be said that the moving speeds of vehicles significantly affect the performance of DSRC systems due to the influence of the Doppler effect. After ANN training, if speeding up the velocity, the throughputs of 4×4 MIMO-OFDM system are increasing at lower data sample training. © 2025 ieee.

关键词： Orthogonal frequency division multiplexing

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Identification and Behavior Pattern Recognition of Cows in intensive and Extensive Farms Using Intelligent Collars and Dimensional Reduction Techniques 19th

Identification and Behavior Pattern Recognition of Cows in I...

引用

19th international conference on Hybrid Artificial Intelligence systems

作者： Michelena, Alvaro Zayas-Gato, Francisco Casteleiro-Roca, Jose-Luis Quintian, Hector Fontenla-Romero, Oscar Luis Calvo-Rolle, Jose Univ A Coruna CTC CITIC Dept Ind Engn Avda19 Febrero S-N Ferrol 15405 A Coruna Spain Univ A Coruna Fac Comp Sci CITIC LIDIA Campus Elvina S-N Ferrol 15071 A Coruna Spain

ISBN: (纸本)9783031741852;9783031741869

The Spanish livestock sector plays a key role in European agricultural production, recently benefiting from investments in advanced technologies to optimize the management and sustainability of the sector. This research work focuses on the analysis of animal behavior, using smart monitoring collars to collect data from dairy cows in intensive and extensive farms. Using dimensional reduction techniques such as PCA, Isomap and t-SNE, this paper compares behavioral patterns between both kinds of farms. The main goal is to identify significant differences in behavior that may influence animal health and efficiency. The results indicate that these techniques can effectively discern between behaviors under different management regimes, which is essential for the implementation of future intelligent models to aid in the early detection of abnormal events such as disease or reproductive *** ability to characterize and differentiate these behaviors underscores the importance of advanced data analysis techniques in the continuous improvement of livestock management, aligning production with more sustainable and responsible practices.

关键词： Monitoring Smart Collars Dimensional Reduction Techniques Cow Behaviour Classification

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Crop Care AI: The Smart Farming Revolution

Crop Care AI: The Smart Farming Revolution

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramkumar, M.V. Mirudula Shri, M. Gowthaman, S.P. Subhashini, J. Srm Institute of Science and Technology Department of Electronics and Communication Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

This research work introduces Crop care AI, an innovative solution designed to enhance precision agriculture. Ground sensors collect critical parameters such as NPK levels, pH, temperature, and rainfall in real-time. This data is then processed using machine learning algorithms to recommend optimal crop types and fertilizer quantities for specific regions. Additionally, the system incorporates a yield prediction model, utilizing environmental and historical yield data to forecast future crop performance. The AI-generated recommendations are accessible through a mobile application, providing personalized guidance for farmers irrespective of their location or expertise. By promoting sustainable farming practices, improving yield accuracy, and offering real-time decision-making support, Crop Care AI aims to revolutionize efficient crop management. © 2025 ieee.

关键词： Smart agriculture

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Crypto Trading Platform: MERN Stack with Real-Time data and Social Features 2

Crypto Trading Platform: MERN Stack with Real-Time Data and ...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Gurupriya, M. Mouhitha, A. Gujjula, Saranya Ankireddypalli, Okesh Reddy Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798331505745

The increasing complexity of cryptocurrency markets necessitates the development of efficient portfolio management tools that provide real-time tracking, price updates, and market awareness. This paper focuses on an advanced concept of a cryptocurrency wallet service provision that provides a summary of available coins, their prices, available quantities, and customers' transaction histories. Developed for the 'frontend' using *** and for the 'backend' using ***, which uses the CoinGecko API for receiving live market prices and collecting curated news blog articles from other APIs. The data related to the wallets are efficiently managed since the application is built based on the MongoDB schema, and in addition to this, the user authentication is also integrated. A responsive design of the user interface allows for showing both a portfolio summary and the latest news related to cryptocurrencies, thus suggesting an 'everything in one place' solution for users interested in cryptocurrencies. This system shows how real-time data integration could be implemented, how the system is friendly to users, and how the system manages the content. This system could be expanded in the future with predictive analytics and an AI recommendation system for the portfolio management and contents. © 2025 ieee.

关键词： Financial markets

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthi, M. Manirajan, S.P. Prince, A. Santhosh, R. Department of Artificial Intelligence and Data Science India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption. © 2025 ieee.

关键词： Lung cancer

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