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Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

Intelligent Automation & Soft Computing 2023年第4期36卷 183-192页

作者： S.Samsudeen Shaffi I.Muthulakshmi Department of Computer Science and Engineering PET Engineering CollegeVallioorTamil Nadu627117India Department of Computer Science and Engineering V.V.College of EngineeringTisaiyanvilaiTamil Nadu627657India

As data grows in size,search engines face new challenges in extracting more relevant content for users’*** a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s ***,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate *** a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher *** paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various *** Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences ***ﬁndings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankﬁnal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

关键词： Weighted pagerank algorithms search engines web pages web crawlers World Wide Web

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An Advanced Brain Tumour Detection Framework Using Improved Fast Mask R-CNN Techniques for Enhanced Accuracy

An Advanced Brain Tumour Detection Framework Using Improved ...

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

作者： Shankar, G. Kumar, V.R. Abhishek Srikanth, Akkaru Reddy, Thummala Jathin Sahil, Shaik Rmd Engineering College Department of Computer Science and Engineering India Rmd Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image processing algorithms. Identification of brain tumors is still a significant difficulty in medical imaging since successful therapy depends on early and accurate identification. By precisely identifying tumor boundaries in brain MRI scans, the proposed model makes use of an enhanced version of FMRCNN is intended to enhance separating and categorizing performance. Sophisticated image processing methods are used to improve the extraction of features, allowing the model to more accurately differentiate between tumor and non-tumor areas. By lowering false positives and true negatives, the structure seeks to maintain computing power while greatly increasing detection rates. The proposed strategy outperforms conventional techniques in terms of reliability, specificity, and sensitivity, according to the results of experiments, which makes it a viable tool for support with clinical choices as well as initial diagnosis in neuroimaging. IFRCNN was applied using the Open CV package in conjunction with an SVM algorithm. Using 218 photos as an initial training set, this study's approach demonstrated a 100% success rate in classifying meningiomas and 87.5% accuracy in classifying gliomas, with an average conviction score of 94.6% in the segments of meningioma tumors. A neurologist does a manual examination and ground truth assessment to verify the divided tumor areas. © 2024 IEEE.

关键词： Convolutional neural networks

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Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network

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Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 509-517页

作者： LONARE Savita BHRAMARAMBA Ravi Pimpri Chinchwad College of Engineering Pune 411044India Department of Computer Science and Engineering GITAMViskhapatnam 530045India

Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning ***,data privacy and security are always a challenge in every field where data need to be uploaded to the *** learning(FL)is an emerging trend for distributed training of *** primary goal of FL is to train an efficient communication model without compromising data *** traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic *** paper presents FL-based traffic flow prediction with spatio-temporal *** work uses a differential privacy(DP)scheme for privacy preservation of participant's *** the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP *** proposed framework trains the data locally at the client-side with *** then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained *** results of the proposed work show that the FedGCN model accurately predicts the *** scheme at client-side helps clients to set a budget for privacy loss.

关键词： federated learning(FL) traffic flow prediction data privacy graph convolutional network(GCN) differential privacy(DP)

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Harnessing Blockchain to Address Plasma Donation Network Challenges

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computers, Materials & Continua 2023年第7期76卷 631-646页

作者： Shivani Batra Mohammad Zubair Khan Gatish Priyadarshi Ayman Noor Talal H.Noor Namrata Sukhija Prakash Srivastava Department of Computer Science and Engineering SRM UniversityDelhi-NCR1310029India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia Department of Computer Science and Engineering KIET Group of InstitutionsDelhi-NCRGhaziabad201206India College of Computer Science and Engineering Taibah UniversityMadinahSaudi Arabia College of Computer Science and Engineering Taibah UniversityYanbuMadinahSaudi Arabia Department of Computer Science and Engineering Banasthali VidyapithRajasthan304022India Department of Computer Science and Engineering Graphic Era(Deemed to Be University)Dehradun248002India

Plasma therapy is an extensively used treatment for critically unwell *** this procedure,a legitimate plasma donor who can continue to supply plasma after healing is ***,significant dangers are associated with supply management,such as the ambiguous provenance of plasma and the spread of infected or subpar blood into medicinal ***,from an ideological standpoint,less powerful people may be exploited throughout the contribution ***,there is a danger to the logistics system because there are now just some plasma *** research intends to investigate the blockchain-based solution for blood plasma to facilitate authentic plasma *** parameters,including electronic identification,chain code,and certified ledgers,have the potential to exert a substantial,profound influence on the distribution and implementation process of blood *** understand the practical ramifications of blockchain,the current study provides a proof of concept approach that aims to simulate the procedural code of modern plasma distribution ecosystems using a blockchain-based *** agent-based modeling used in the testing and evaluation mimics the supply chain to assess the blockchain’s feasibility,advantages,and constraints for the plasma.

关键词： Blockchain hyperledger fabric information visibility plasma donation network plasma quality

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AI-Enhanced Precision Crop Rotation Management for Sustainable Agriculture

AI-Enhanced Precision Crop Rotation Management for Sustainab...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Hafiyya, R.M. Jemsheer Ahmed, P. Nabhan, P.A. Melethil, Safvan Mohammed, Nadhi Muhammed Murshid, A.P. Mea Engineering College Computer Science & Engineering India

ISBN: (纸本)9798350394399

This paper presents a novel approach to crop rotation management that integrates cutting-edge artificial intelligence (AI) techniques to improve and modernise agricultural processes. The study seeks to revolutionise traditional crop rotation tactics by utilising state-of-the-art machine learning techniques, namely supervised learning approaches. Through the use of real-time weather forecasts and historical crop performance data, AI-powered technology provides farmers with tailored advice for maximising crop rotations, ultimately leading to better agricultural results. The methodical methodology includes planning the project, gathering data, creating AI models, and deploying the system. Establishing precise project objectives, collecting and analysing soil data with care, creating AI-driven recommendations, and making sure that monitoring and improvement are ongoing are all crucial tasks. At every level, the system uses a range of instruments and software to guarantee a methodical, data-driven approach that produces improved crop rotation techniques that are advantageous to the environment and the agricultural industry. This study adds to the rapidly developing field of artificial intelligence in agriculture by offering a workable and creative answer to the problems farmers face when managing crop rotation. The results underline how AI may be used to improve farming techniques, increase crop yields, and promote sustainable farming, which makes it an important contribution to the field of precision agriculture research. © 2024 IEEE.

关键词： Crop rotation

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Topological Evaluation of Certain computer Networks by Contraharmonic-Quadratic Indices

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computers, Materials & Continua 2023年第2期74卷 3795-3810页

作者： Ahmed M.Alghamdi Khalid Hamid Muhammad Waseem Iqbal M.Usman Ashraf Abdullah Alshahrani Adel Alshamrani Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering at University of JeddahJeddah21493Saudi Arabia

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two *** kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify *** number that can be entirely calculated by a graph is called graph *** mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty ***,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic *** this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or *** study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

关键词： Bridge networks invariants Quadratic-Contraharmonic Indices maple network graph molecular graph

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AI-Optimized Eco-Charge Regeneration Technology in EV 6

AI-Optimized Eco-Charge Regeneration Technology in EV

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sheeja, R. Akshaya, R. Akshaya, S. Jashwanthikaa, D.R. Easwari Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350306613

In the modern era of transportation, improving the energy efficiency poses to be one of the major challenges faced by electric vehicles (EVs). When drivers apply brakes the braking systems convert kinetic energy into heat which leads to loss of power and energy. This loss of power hinders the performance and range optimization of EVs. In order to solve this problem, there is a need to find solutions that helps in increasing energy efficiency in EVs. This paper suggests a solution that can be used in optimizing the energy recovered during braking using Artificial Intelligence (AI). The application of AI algorithms helps in redirecting the kinetic energy to the battery resulting in an enhanced energy regeneration process. The proposed AI optimized aims to overcome the limitations of the traditional energy recovery systems. Through real time data-Analysis and adaptable control, the AI system maximizes the conversion of energy and ultimately improves both the energy efficiency and range of anxieties. This approach not only deals with the setbacks of existing braking systems, but also cope with the increasing demand for sustainable technologies EVs. The integration of AI into the process of energy recovery and optimization leads to a transformation in electric vehicle technology. © 2024 IEEE.

关键词： AI and ML algorithms braking force energy consumption energy optimization energy recovery EV

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Genetics Based Compact Fuzzy System for Visual Sensor Network

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computer Systems science & engineering 2023年第4期45卷 409-426页

作者： Usama Abdur Rahman C.Jayakumar Deepak Dahiya C.R.Rene Robin Department of Computer Science and Engineering Jerusalem College of EngineeringChennaiTamil Nadu600100India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringChennaiTamil Nadu602117India College of Computer and Information Sciences(CCIS) Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044India

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static *** from the camera is sent to energy efficient sink to extract key-information out of *** applications range from health care monitoring to military *** a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O *** this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as *** innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the ***,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into *** propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual *** architecture is designed based on Mamdani’s fuzzy *** parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH *** inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of *** proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive *** algorithmbased machine learning optimizes the interpretability aspect of fuzzy *** results are obtained using *** result shows that the classification acc

关键词： Visual sensor network fuzzy system genetic based machine learning mobile sink efficient energy life of network

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A Refuge System Development for Underground Mines through IIOT 2

A Refuge System Development for Underground Mines through II...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Srinath Yadav, K. Vignesh, M. Sivaguru, S. Ranjeeth Kumar, G. Santhosh, P. VSB Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350368109

Underground mines can use the Internet of Things to monitor the environment, safety, and productivity by installing sensors that detect temperature, moisture content, and gas levels in real time. These sensors collect data continually and relay it to a central monitoring system. By examining this data to discover any anomalous conditions such as high temperatures, excess moisture, or harmful gas concentrations, mine managers can take immediate corrective action to preserve worker safety, prevent environmental damage, and enhance production efficiency. Our real-time abstract monitoring system provides essential insights into underground mining operations, helping to decrease hazards and enhance operational effectiveness. © 2024 IEEE.

关键词： Mine safety

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