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检索条件"机构=Dept. Informatics Engineering of Computer Science"

共 36913 条记录，以下是171-180 订阅

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An IoT Solution for Enhanced Tray Management Systems in Food Service and Industrial Settings

An IoT Solution for Enhanced Tray Management Systems in Food...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Chaudhary, Vipin Kumar Singh, Suchita Singh, Nagendra Pratap Afreen, Sana Yadav, Manmohan Singh Gupta, Durgesh Kumar Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science and Engineering Uttar Pradesh Gorakhpur India Dr. B R Ambedkar National Institute of Technology Dept. of Computer Science and Engineering Punjab Janlandhar India Telangana Hyderabad India Sharda University Dept. of Computer Science and Engineering Uttar Pradesh Greater Noida India Institute of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Sitapur India

ISBN: (纸本)9798350385199

This study introduces TraySmart, a cutting-edge Internet of Things (IoT) tool meant to completely transform the effectiveness and administration of tray systems in industrial, food court, and cafeteria environments. TraySmart employs cutting-edge technologies like weight sensing, RFID tag reading, temperature monitoring, NFC for user interaction, and Wi-Fi Bluetooth for data transmission to tackle the widespread issues associated with tray management, such as inefficient operations, concerns about food safety, and less-than-ideal user experiences. The weight-sensing feature of the device makes it possible to identify objects on the tray and monitor the contents of the tray in real time. Reading RFID tags makes identifying and tracking items easier, improving inventory control and lowering loss. Food safety and quality assurance depend on food items maintaining their ideal temperatures, which is ensured via temperature monitoring. Furthermore, NFC technology enhances the eating experience by enabling users to engage with the smartphone for payments or feedback. The communication options on the device guarantee smooth data transfer to a central system, facilitating thorough data analysis and system optimization. This essay covers the creativity, design, and development of the TraySmart prototype, the difficulties faced, and the strategies used in the process. The report assesses TraySmart's effect on operational efficiency, food safety standards, and customer happiness by carefully examining the outcomes of pilot testing. With its scalable solution to improve tray management systems, TraySmart marks a significant leap in the application of IoT technology in the food service and industrial sectors. The results highlight how IoT gadgets like TraySmart have the power to revolutionize operational procedures and service delivery, establishing a new benchmark for innovation in the industry. © 2024 IEEE.

关键词： Customer satisfaction

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Survival Prediction for Patients with Cirrhosis

Survival Prediction for Patients with Cirrhosis

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sowmiya, X.L. Sanjana, R. Nishintha, A. Pavithra, S. Kannan, Shree Charan Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331510022

Cirrhosis is one of the major causes of death around the world. Hence, the possibility of survival prediction among patients affected by this disease constitutes the principal factor in properly planning treatment and management procedures. This work aims to design a predictive model of the probability of survival of patients who have cirrhosis based on patient data, including clinical parameters, medical history, and laboratory findings of these patients. The data-driven machine learning algorithm improves the predictive accuracy and significantly enhances the clinical decision-making process. This considers multiple predictors: the patient's liver function tests, the demographic factors, and the indicators of disease progression in the body. It will be validated for reliability and accuracy using real-world patient case data. Therefore, this tool could aid clinicians in devising treatment plans that improve patient outcomes and efficiently utilise healthcare resources. This study also contributes to the emerging genre of AI-assisted care because chronic liver disease management, specifically cirrhosis, in this case, requires constituting predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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Exploring Climate Change Prediction Dynamics Using Machine Learning Approach: A Review 1

Exploring Climate Change Prediction Dynamics Using Machine L...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tiwari, Shashank Verma, Shikha Chauhan, Rosey Gupta, Neha Sharda Unviersity Dept. of Computer Science Gerater Noida India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Climate refers to the long-term weather patterns of a region. Variations in the weather have a lot of implications on our daily lives. Explore the application of deep learning techniques in climate prediction and related fields. It focuses on Long Short-Term Memory (LSTM) models for forecasting air quality, greenhouse temperature control, and urban flood water accumulation using Gradient Boosted Decision Trees (GBDT). The analysis concludes that deep learning, particularly LSTM, offers promising accuracy for various climate-related predictions. Challenges such as data scarcity, non-stationarity, and high dimensionality are addressed, highlighting the need for advanced models to capture complex climate system dynamics and human influences. © 2024 IEEE.

关键词： Prediction models

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-dept. examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Privacy-Enhanced Image Restoration in Remote Sensing via Federated Learning 12

Privacy-Enhanced Image Restoration in Remote Sensing via Fed...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Jahanzeb Khan, M. Rath, Suman Zaib, Muhammad Hassan University of Nevada Reno Dept. of Computer Science and Engineering Reno United States Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350330366

This research addresses the dual challenges of image restoration quality and data privacy in optical remote sensing. Traditional restoration methods often fall short due to the complex nature of remote sensing images, and data privacy concerns further complicate the use of advanced techniques. By integrating the Deep Memory Connected Neural Network (DMCN) with the Data-Decoupled Federated Learning (DDFL) framework, our approach enables significant improvements in image restoration without requiring direct access to sensitive raw data. This method not only enhances data privacy by leveraging federated learning principles but also incorporates advanced techniques like Gaussian image denoising to maintain high restoration quality despite potential noise introduced by the federated process. The performance of the federated DMCN, particularly on the UCMERCED dataset, demonstrates minimal accuracy degradation, even in the presence of noise, while the strategic use of Downsampling Units within DMCN optimizes computational efficiency. Our comprehensive evaluations reveal the effectiveness of this approach in balancing data privacy with the need for high-quality image restoration, suggesting a promising direction for future advancements in remote sensing applications. © 2024 IEEE.

关键词： Image reconstruction

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Multi-Layered Approach to IoT Cybersecurity in Smart Cities 3

Multi-Layered Approach to IoT Cybersecurity in Smart Cities

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Benjamine, Daniel Anto Madhavan, K. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (纸本)9798331527549

Internet of Things (IoT) has been rapidly growing over the past years. There have been many cybersecurity challenges faced by IoT-based smart cities in existing work for the energy, health and transportation sectors. These cybersecurity challenges are crucial to remove in the IoT sector to implement a safe and secure IoT based smart city. Vulnerabilities across layers in IoT are analyzed and a multi-layered model of security is proposed to enhance data integrity and communication security by using AI, blockchain and ML. This paper would also present several policy recommendations to stakeholders and IoT developers for building a safe urban IoT ecosystem. © 2025 IEEE.

关键词： Cybersecurity frameworks IoT smart city technologies urban

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kumar, Ch Sree Singh, Akhilendra Pratap Reddy, K Hemant K National Institute of Technology Meghalaya Dept. of Computer Science Meghalaya India VIT-AP Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350369175

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes. © 2024 IEEE.

关键词： Distributed ledger

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Dots to Dialogue: Enhancing Accessibility through Braille Image-to-Speech Conversion 3

Dots to Dialogue: Enhancing Accessibility through Braille Im...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shwetha, B.S. Sirisha, M. Vachana, U. Kadlimatti, Aditya Manjushree, N.S. Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (纸本)9798331527549

Braille script serves as a fundamental medium of communication for individuals with visual impairments. However, its interpretation by non-experts poses a significant challenge, hindering inclusivity in education and daily interactions. This study proposes a system that leverages Convolutional Neural Networks (CNNs) and the Google Text-to-Speech (GTTS) library to translate Braille text into multilingual speech. This research addresses the challenge of Braille literacy among visually impaired individuals by developing a system that translates Braille text into multilingual speech. The proposed solution utilizes advanced CNNs for accurate Braille character recognition and integrates GTTS technology for real-time speech output. The results demonstrate a significant improvement in accessibility and communication for users, with an accuracy rate of 97.28% achieved using the AlexNet model." © 2025 IEEE.

关键词： Convolutional neural networks

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