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Sustainable Expert Systems (ICSES), International Conference on

作者： V.S. Saranya Garimidi Subbarao Dega Balakotaiah Maloth Bhavsingh K. Suresh Babu Srinivasa Rao Dhanikonda Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Department o Computer Science and Engineering (AIDS) Pace Institute of Technology and Sciences Department of Computer Science and Engineering Vignan's Founadation for Science Technology & Research (Deemed to be University) Department of Computer Science and Engineering Ashok: Womens Engineering College Kurnool Andhra Pradesh Department of Computer Science and Engineering Narasaraopeta Engineering College(Autonomous) Narasaraopet A.P Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education (Deemed to Be University) Hyderabad Telangana India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts

关键词： Adaptation models Analytical models Accuracy Smart cities Urban planning Data models Real-time systems Internet of Things Optimization Context modeling

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An Investigation of IOT-Based Consumer Analytics to Assist Consumer Engagement Strategies in Evolving Markets 3

An Investigation of IOT-Based Consumer Analytics to Assist C...

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3rd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2023

作者： Jayadeva, Sujay Mugaloremutt Prasad Krishnam, Nagendra Raja Mannar, B. Prakash Dabral, Amar Buddhi, Dharam Garg, Neha Jss Academy of Higher Education & Research Department of Health System Management Studies Faculty of Management Studies India Telangana Hyderabad India St. Theresa Intl College Business Administration Thailand Graphic Era Deemed to Be University Department of Hospitality Management Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In order to establish sustainable operations, Internet innovation has been widely adopted in the retail sector;however, certain operational difficulties, such as security and confidentiality have been uncovered in practise. As a result, linked fields must do more thorough theoretical study. In order to compete with online merchants and promote sustainable growth, this study examines how traditional brick and mortar stores might use IoT technology. First, by taking into account the costs of customer inconvenience associated with shopping through various retail partners and the influence of Technology, pricing Plans were created for the store and the retailer of concrete blocks, respectively. Furthermore, by contrasting different scenarios, the benefits of internet to retail shops and consumer excess are analysed. After that, the basic models were expanded to take into account consumer - perceived sensitivity regarding IoT technology and price competitiveness in order to test the robustness of the analytical conclusions. Additionally, actual data-based numerical analyses are performed. © 2023 IEEE.

关键词： Internet of things

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UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration

arXiv

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arXiv 2025年

作者： Huang, Chung-Ju He, Yuanpeng Han, Xiao Jiao, Wenpin Jin, Zhi Wang, Leye Key Lab of High Confidence Software Technologies Ministry of Education Beijing100816 China School of Computer Science Peking University Beijing100871 China School of Economics and Management Beihang University Beijing100191 China

Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing of sensitive patient information between hospitals. Vertical federated learning (VFL) offers a novel privacy-preserving machine learning paradigm that maximizes data utility across multiple hospitals. Traditional VFL methods, however, primarily benefit patients with overlapping data, leaving vulnerable non-overlapping patients without guaranteed improvements in medical prediction services. While some knowledge transfer techniques can enhance the prediction performance for non-overlapping patients, they fall short in addressing scenarios where overlapping and non-overlapping patients belong to different domains, resulting in challenges such as feature heterogeneity and label heterogeneity. To address these issues, we propose a novel unified vertical federated knowledge transfer framework (Unitrans). Our framework consists of three key steps. First, we extract the federated representation of overlapping patients by employing an effective vertical federated representation learning method to model multi-party joint features online. Next, each hospital learns a local knowledge transfer module offline, enabling the transfer of knowledge from the federated representation of overlapping patients to the enriched representation of local non-overlapping patients in a domain-adaptive manner. Finally, hospitals utilize these enriched local representations to enhance performance across various downstream medical prediction tasks. Experiments on real-world medical datasets validate the framework’s dual effectiveness in both intra-domain and cross-domain knowledge transfer. The code of UniTrans is available at https://***/Chung-ju/Unitrans. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

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computers, Materials & Continua 2021年第5期67卷 2393-2407页

作者： J.Banumathi A.Muthumari S.Dhanasekaran S.Rajasekaran Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Information Technology University College of EngineeringNagercoil629004India Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Electrical Engineering PSN College of EngineeringTirunelveli627152India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly *** classification of HSI becomes a critical process in the domain of hyperspectral data *** massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground ***,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high *** this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC ***,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier ***,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML *** addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class *** extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University *** obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

关键词： Hyperspectral imagery deep learning xception kernel extreme learning map parameter tuning

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Organic-Perovskite Bilayer Color Conversion Media for Full-Color Displays

Organic-Perovskite Bilayer Color Conversion Media for Full-C...

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International Conference on Display Technology, 2024

作者： Liu, Siyang Ting Tam, Bryan Siu Zhu, Mingchao Ye, Fanghao Yan Yeung, Fion Sze Li, Guijun Dong, Shou-Cheng State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Shenzhen China Institute for Advanced Study Hong Kong University of Science and Technology Kowloon Hong Kong Co. Ltd. Suzhou China

Color conversion media (CCM) are crucial for realizing cost-effective, high-resolution micro-LED and OLED displays. In this work, we developed green and red organic-perovskite bilayer CCM that combine the PLQY and color purity of perovskite quantum dots (PeQDs) and the strong blue absorption of perylene-based organic emitters. The bilayer with thickness of 300 nm showed over 99% blue absorption and PLQY of 77% and 61% for green and red CCM, respectively. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Perovskite

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Crop and Fertilizer Recommendation System Applying Machine Learning Classifiers

Crop and Fertilizer Recommendation System Applying Machine L...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： S. Iniyan M. Senthil Raja R. Srinivasan C. Santhanakrishnan Aravindan Srinivasan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kaatankulathur Tamilnadu India Department of Computer Science and Engineering Lakshmaiah Education Foundation Vaddeswaram Andrapradesh India

In today's agriculture, increasing crop yields while minimizing resource use is critical for sustainable and profitable agriculture. The program addresses the urgent need for personalized crop and fertilizer recommendations in various agricultural regions of India. Using machine learning techniques such as linear regression, random forests, k-nearest neighbors and outlier detection, our model aims to provide farmers with clear guidance based on various environmental factors and soil. Linear regression algorithm is used to establish the relationship between input and product yield, K-Neighbors adds recommendations by identifying the similarity between farm features and neighboring farm features. This holistic approach is designed to increase the credibility of our proposal, given the unique challenges posed by India's diversity and climate. The model accepts soil characteristics, climate, crop history and fertilizer use patterns. We worked to create a variety of useful and flexible products that can be more efficient and practical and provide good results to farmers in different regions. The need of the project is an easy-to-use website that provides farmers with information about crop selection and planting. By leveraging the power of machine learning, the program helps modern agriculture, promotes sustainable practices and increases the overall profitability of Indian agriculture.

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Effective Seizure Recognition from EEG Data by Asymmetrical Back Propagation Neural Network (ABPN) Classification

Effective Seizure Recognition from EEG Data by Asymmetrical ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Prateek Gupta Dinesh Mishra Bhupchand Kumhar Prashant Kumar Shukla Naveen K B Lipika Roy Department of Computer Science and Engineering Shri Ram Institute of Science and Technology Jabalpur Madhya Pradesh Department of Computer Science and Engineering Mangalyatan University-Jabalpur Jabalpur MP India Department of Computer Science and Engineering IES Institute of Technology and Management IES University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electronics and Communication Engineering BGS Institute of Technology Adichunchanagiri University Gyan Ganga Institute of Technology and Sciences Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

A significant task within neurology and epilepsy management is the differentiation of seizures from the Electroencephalogram (EEG signal). The current work of research focuses on suggesting an idea to enhance the detection of seizures by applying the Asymmetrical Back Propagation Neural Network (ABPN) classification. This dynamic aspect of ABPN is ideal for learning the details of the features and patterns of EEG since seizures are usually unilateral. We also compare our ABPN classifier with the standard neural networks to confirm that our proposed classifier perform better in sensitivity, specificity, and the overall assessment of the classifier in the global environment. According to the experiment, the authors are convinced that the ABPN model can solve the problem of lowering the false-positive rate along with the increase in the speed of detection to make it suitable for real-time seizure observation. This research will be useful in the improvement of better equipment to detect the seizures with a positive implication on patient health.

关键词： Backpropagation Neurology Sensitivity Government Epilepsy Brain modeling Electroencephalography Real-time systems Biological neural networks Standards

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Simulated Boron Shimming Cyberattack on Pressurized Water Reactor

Simulated Boron Shimming Cyberattack on Pressurized Water Re...

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2022 Transactions of the American Nuclear Society Winter Meeting and Technology Expo, ANS 2022

作者： Root, Sam J. Throckmorton, Porter Haney, Michael Borrelli, R.A. Nuclear Cybersecurity Working Group University of Idaho Idaho Falls Center for Higher Education 1776 Science Center Drive Idaho FallsID United States Department of Nuclear Engineering and Industrial Management United States Department of Computer Science United States

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Dynamic Trust management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： D J Samatha Naidu Upma Jain Shruthi S K. B. V. Brahma Rao Syed Mohammad Uzair Iqbal Natrayan L Department of Master of Computer Applications Annamacharya PG College of Computer Studies India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation India Department of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Mechanical Engineering Saveetha School of Engineering SIMATS India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS).

关键词： Access control Fuzzy logic Performance evaluation Routing Throughput Device-to-device communication Encryption Internet of Things Servers Trust management

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