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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Bhimraoji Sadafale, Kishor Thorat, Sandeep. Anandrao. Smt. Kashibai Navale College of Engineering Computer Engineering Maharashtra Pune India Government College of Engineering and Research Computer Engineering Avasari Kh 412405 India

ISBN: (纸本)9798350372748

Most of the data created by humans comes from blogs, digital magazines, social networking sites, and news websites. Extracting the essential information from a text through hand summarization is time-consuming and overwhelming. In general, a text summary is a technique for producing a condensed or shortened version of a text document that includes pertinent information for readers. Summarization approaches use computational techniques to produce a reduced version of a text while retaining its original meaning. As social networking sites, eBooks, and e-papers continue to expand, transliterated terms are becoming more and more common in text corpora. This research study intends to conduct a review of text summarization by reviewing 20 papers. The review includes the following contribution. (i) Reviews the methods (machine learning/deep learning/clustering techniques) used for text summarization in the selected papers. (ii) Reviews the features considered for the summarization process. (iii) Reviews the languages considered. (iv) Reviews the performance metrics used and the maximum performance obtained by the methods. (v) Determines the research gaps and challenges. © 2024 IEEE.

关键词： Deep learning

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Improving Network Security with Gradient Boosting from KDD Cup Dataset

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SN computer Science 2024年第7期5卷 877页

作者： Dwivedi, Devanshi Bhushan, Aditya Singh, Ashutosh Kumar Snehlata Department of Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Allahabad 211010 India

Network traffic analysis plays a crucial role in detecting and mitigating security threats in modern computer networks. In this study, we employ machine learning techniques, specifically Gradient Boosting, Linear Discriminant Analysis (LDA), and Support Vector Machines (SVMs), to analyze network traffic data from the KDD Cup dataset. The KDD Cup dataset contains a large volume of network traffic data, including various features such as duration, protocol type, service, and flag, among others. We first preprocess the dataset by removing redundant rows and converting categorical variables to factors. We then split the dataset into training and testing sets and perform feature scaling to ensure uniformity across features. For our analysis, we utilize Gradient Boosting, a powerful ensemble learning technique, to iteratively train weak learners to correct the errors of the previous models. Additionally, we employ Linear Discriminant Analysis to find the linear combination of features that best separates the classes, and SVMs with both linear and Radial Basis Function (RBF) kernels for classification. We evaluate the performance of each model using standard metrics such as accuracy, precision, recall, and F1-score. Furthermore, we visualize the loss percentage over iterations for Gradient Boosting to gain insights into the optimization process. Our results demonstrate the effectiveness of Gradient Boosting, LDA, and SVMs in accurately classifying network traffic instances. The models show promising performance in distinguishing between normal and malicious network activity, highlighting their potential for enhancing network security in real-world applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity Gradient boosting model KDD Cup dataset Network traffic Support vector machines

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Cloud storage framework for multivariate regression-based data mining: optimised LIFR and FIFR model

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International Journal of Web engineering and Technology 2025年第1期20卷 66-99页

作者： Sarkar, Saswati Kundu, Anirban Computer Innovative Research Society West Bengal 711103 India Netaji Subhash Engineering College Kolkata700152 India

Authors propose a cloud-based storage framework to search data using optimised mapping. Multivariate regression-based data mining mechanism shows the utility of map tables, storage partitions, and data storage in cloud. The paper exhibits complexity of cloud-based searching algorithms in real-time scenario. The proposed cloud-based storage framework represents parallel disk searching technique to search data with less time consumption. Proposed technique exhibits linear time complexity. Drive partition concepts and map table concepts have been incorporated in this work for searching data in less time. It is observed that the overall execution time is inversely proportional to the number of drive partitions. Sequential and several parallel situations exhibit the comparisons using time graphs. The paper presents comparison graphs for execution time and time complexity between existing techniques and proposed approach with respect to storage partitions. Experimental observations and analysis using statistical data have been shown in this paper. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cloud storage

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Prediction and Optimization of Thermal Conductivity and Viscosity of Stable Plasmonic TiN Nanofluid Using RSM and ANN Combined Approach for Solar Thermal Applications 1

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8th International Conference on Advances in Energy research , ICAER 2022

作者： Deshmukh, Kishor Karmare, Suhas Trinity College of Engineering and Research Pune India Amrutvahini College of Engineering Sangamner India Government College of Engineering Awasari India

ISBN: (数字)9789819922796

ISBN: (纸本)9789819922789

Nanofluids open up a new dimension in solar thermal applications because of their immense thermophysical properties. Stable, efficient, and low-cost nanofluid preparation is a new research topic. Titanium nitride nanoparticles possess localized surface plasmon resonance properties. It enables a higher level of photoabsorption. TiN particles with a size of 40–50 nm were used to prepare distilled water-based nanofluid at concentrations of 0.025, 0.05, 0.075, and 0.1%. TiN nanofluids and base fluid thermal conductivity and viscosity are measured experimentally at temperatures ranging from 30 to 50 °C. Experimentation to determine TiN nanofluid thermophysical properties is time-consuming. The current study uses the surface response approach and an artificial neural network to estimate the thermal conductivity and viscosity of a water-based, stable plasmonic TiN nanofluid. The ANOVA tool examined the significance of input variables and their interactions. The performance of both predictive models was tested in terms of correlation coefficient (R2) and mean square error (MSE) to determine the best fit. The best-fit model was selected among the RSM and ANN models. The surface response approach improves process parameters for maximum heat transfer by utilizing trustworthy and efficient model findings. The maximum thermal conductivity of 0.78061 W/mK and minimum viscosity of 0.8788 cP obtained at 55 °C and 0.05252% volume concentration. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Viscosity

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Aqua Status Prediction Using IoT and Optimization in Aquaculture: A Comprehensive Review

Aqua Status Prediction Using IoT and Optimization in Aquacul...

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Londhe, Aoudumber Apare, Ravindra Borhade, Ravindra Savitribai Phule Pune University Smt. Kashibai Navale College of Engineering Department of Computer Engineering Pune India Savitribai Phule Pune University Trinity College of Engineering and Research Department of Information Technology Pune India

ISBN: (纸本)9798350362879

The issue of decreasing safe water resources, which has detrimental effects on both human and aquatic life, is of utmost importance. The advent of IoT-based smart quality monitoring system for water presents a promising solution to this problem. These systems utilize advanced technology to provide instantaneous data on water quality, facilitating the identification and prompt response to issues such as contamination and pollution. Parameters such as pH levels, turbidity, dissolved oxygen, temperature, and pollutant concentrations are continuously monitored by these IoT-based systems, enabling timely detection of changes in water quality. In contrast to traditional methods involving manual sampling and laboratory testing, IoT-based systems are often more cost-effective in the long term as they reduce the necessity for frequent on-site visits and lab testing. By enabling quicker detection and response to contamination events, IoT-based water quality monitoring systems can contribute to a reduction in the environmental impact of water pollution. Consequently, these systems hold significant potential in addressing the challenges associated with declining safe water resources. Their real-time monitoring capabilities, cost-efficiency, and scalability make them a valuable tool for various applications, including domestic use. As technology continues to advance, these systems become increasingly effective in preserving water quality and safeguarding aquatic ecosystems. © 2024 IEEE.

关键词： Water quality

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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Maximizing Learning Outcomes while Minimizing: Costs: A Cost-Effective Approach to M-Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第11期18卷 4-14页

作者： Suresh Kumar, B. Hemawathi, M.S. Dhanasekaran, P. Kaliappan, Vishnu Kumar Sivaganesan, D. Department of Electronics and Communication Engineering The Kavery Engineering College Tamil Nadu Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibil- ity offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and per- sonalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without com- promising the quality of educational delivery. Ultimately, the findings highlight the transfor- mative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. © 2024 by the authors of this article.

关键词： Contrastive Learning

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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