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Spectrum Management for Wireless Resources in 5G Networks using Gated Graph Convolutional Network and Whale Optimization Algorithm

Spectrum Management for Wireless Resources in 5G Networks us...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Kuzhaloli S Hyba AbdulJalee Lanka Padma Yogesh Ramaswamy K. Radha Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of AI & DS Vivekananda College of Engineering for Women Namakkal India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Recently, Artificial Intelligence (AI)-driven spectrum management in 5G networks leverages learning techniques to reduce allocation of resources and identify the patterns in traffic that leads to effective communication of wireless devices. However, it faced challengers include complex network and providing security to data. To overcome these challenges, various Machine Learning (ML) techniques are used to recognize patterns from complex data and make accurate predictions and Deep Learning (DL) techniques used to handle high dimensional data. Initially, the Multiple Input Multiple Output (MIMO) signal transmission is processed through Proximal Policy Optimization (PPO) for dynamic beamforming. Further, channel estimation is performed using Gated Graph Convolutional Network (GGCN) to enhance multi-user spectrum efficiency. Finally, Improved Whale Optimization Algorithm (IWOA) is utilized for spectrum optimization. The proposed GGCN-IWOA model acquired better results with 5.3 Mean Square Error (MSE), 4.2 Mean Absolute Error (MAE) and 2.8 Root Mean Square Error (RMSE) when compared to Convolutional Neural Network with Gated Recurrent Units (CNN-GRU) model respectively.

关键词： Graph convolutional networks Array signal processing 5G mobile communication Complex networks Logic gates Whale optimization algorithms pattern recognition Communication system security Optimization Radio spectrum management

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Transfering RGB-D Models to RGB by Multi-View Embedding Space Learning for Object Detection

Transfering RGB-D Models to RGB by Multi-View Embedding Spac...

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international conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Jinde Liu Zhang Zhang National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing Beijing China

In this paper, we propose a new framework for detecting objects in RGB images captured by conventional cameras by leveraging a set of labeled RGB-D data. We formulate this problem into a new multi-view learning problem, where we have the depth information in the training set that is not available in the testing set. To solve this problem, we use a three-view CCA learning method which combines visual features, depth features, and label information together to get the projection matrices. Sufficient experiments are done to demonstrate the effectiveness of our method, and improvement measures in the future are also proposed.

关键词： Training Learning systems Visualization Object detection Learning (artificial intelligence) Cameras Manufacturing

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Supervised Machine Learning: Discriminative Learning Methods for pattern Classification using HELM

Supervised Machine Learning: Discriminative Learning Methods...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： N. Shirisha Ajmeera Kiran A. Prashanthi MrKamjula Lakshmi Kanth Reddy MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer Science and Engineering Nalla Narasimha Reddy Education Society's group of Institutions Department of Artificial Intelligence Anurag University Hyderabad

The ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. This work demonstrates the data pre-processing along with the prediction of a churn model. The model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. This research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. The findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness.

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A New Multi-filter Framework with Statistical Dense SIFT Descriptor for Spoofing Detection in Fingerprint Authentication Systems 20th

A New Multi-filter Framework with Statistical Dense SIFT Des...

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20th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Contreras, Rodrigo Colnago Nonato, Luis Gustavo Boaventura, Maurilio Gasparotto Boaventura, Ines Aparecida Coelho, Bruno Gomes Viana, Monique Simplicio Univ Sao Paulo BR-13566590 Sao Carlos SP Brazil Sao Paulo State Univ BR-15054000 Sao Jose Do Rio Preto SP Brazil NYU 550 1St Ave New York NY 10012 USA Univ Fed Sao Carlos BR-13565905 Sao Carlos SP Brazil

ISBN: (纸本)9783030878979;9783030878962

Fingerprint-based authentication systems represent what is most common in biometric authentication systems. Today's simplest tasks, such as unlocking functions on a personal cell phone, may require its owner's fingerprint. However, along with the advancement of this category of systems, have emerged fraud strategies that aim to guarantee undue access to illegitimate individuals. In this case, one of the most common frauds is that in which the impostor presents manufactured biometry, or spoofing, to the system, simulating the biometry of another user. In this work, we propose a new framework that makes two filtered versions of the fingerprint image in order to increase the amount of information that can be useful in the process of detecting fraud in fingerprint images. Besides, we propose a new texture descriptor based on the well-known dense Scale-Invariant Feature Transform (SIFT): the statistical dense SIFT, in which their descriptors are summarized using a set of signal processing functions. The proposed methodology is evaluated in benchmarks of two editions of LivDet competitions, assuming competitive results in comparison to techniques that configure the state of the art of the problem.

关键词： Liveness detection Spoofing detection Fingerprint authentication system Dense SIFT pattern recognition

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An Implementation of RFID-Enabled ID Cards in the Education Sector to Optimize and Facilitate the Use of Student Cards

An Implementation of RFID-Enabled ID Cards in the Education ...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Riskhan, Basheer Zali, Nurul Ain Athirah Hussain, Khalid School of Computing and Informatics Albukhary International University Jalan Tune Abdul Razak Kedah Alor Setar Malaysia

ISBN: (纸本)9789819780921

This chapter proposes the implementation of radio-frequency identification (RFID) technology to enhance various aspects of campus life at Albukhary international University (AIU). The integration of RFID tags in student ID cards forms the basis for an automated attendance and information system, access control to university facilities, library management, a cashless payment system, and a hostel power-switching card system. The RFID-enabled ID cards, equipped with unique identifiers, facilitate seamless interaction with RFID readers strategically placed in classrooms, libraries, and other key areas. The system not only automates attendance tracking but also provides real-time data for comprehensive reports and analytics. Additionally, RFID tags are employed to streamline access to university facilities, manage library resources efficiently, enable cashless transactions, and enhance energy efficiency in hostels. While the implementation offers numerous benefits, the chapter acknowledges potential drawbacks such as installation costs, maintenance requirements, and security concerns. The proposed RFID-based system promises to revolutionize campus operations, offering improved security, convenience, and operational efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authorization

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MyRecommender: Personalized Activity Recommendation based on Negative Emotions Classification

MyRecommender: Personalized Activity Recommendation based on...

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international conference on Information Technology Research (ICITR)

作者： Zaidh Shafraz Hiroshan Ilangeshwaran M.R.M Shifan Chanul Vithanage Lakmini Abeywardana Samadhi Rathnayake Rizka Ismath Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty Graduate Studies University of Colombo Colombo Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

The solution presented attempts to address the crucial problem of identifying and alleviating negative emotions among middle-class population. Our novel method makes use of machine learning and unsupervised learning techniques. Composed of four parts, the first three are text, audio, or video classification to analyze a person's emotional state. The fourth component is a recommendation system offering personalized recommendations to help users alleviate themselves from the negative emotion faced. The middle-class population is increasingly concerned about the impact the country's economic crisis and difficult living conditions may have on their physical and mental health as they work to support their families daily. Understanding how urgent it is to solve this urgent problem; our approach provides useful techniques to reduce and lessen these emotional loads as well as early identification of negative emotions making it easier to alleviate their mental state. A happier and more productive group of people is what this all-encompassing approach promises to bring about. Our system's scalability and adaptability to different situations also enable customers to adapt it to suit their own requirements and preferences.

关键词： Emotion recognition soft sensors Scalability Text categorization Refining Reinforcement learning Mental health Robustness Recommender systems Unsupervised learning

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Specialized software Tool for pattern recognition of Biological Objects 11th

Specialized Software Tool for Pattern Recognition of Biologi...

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11th Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020

作者： Kulik, Sergey D. Levin, Evgeny O. National Research Nuclear University MEPhI Kashirskoe shosse 31 Moscow115409 Russia Sretenka st. 29 Moscow127051 Russia

ISBN: (纸本)9783030655952

This paper describes the results of work on developing the mobile application for recognition of faces and other biological objects. The application is designed with a focus on loading external machine learning models over the Internet, which allows you to change the model without making any modifications to the application. With such realization, the application can be used in many cases. For example, at carrying out conferences: organizers just need to train a model and send out the link for its downloading to all participants of the conference without any changes in source code. Participants will be able to find out the information about other members they are interested in, as well as contact them directly from the application, both by phone and by e-mail. Instructions are given for teaching your own face recognition model using the Microsoft Custom Vision cloud service, which allows you to train regardless of the power of your local computer. As an example, a classification model was trained and the following assessments of recognition quality indicators were obtained precision: 97.8% and recall: 95.8%. In our future work we consider adding the functionality of emotion recognition based on the pattern recognition algorithm, described in this paper. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Leveraging Segment-Anything model for automated zero-shot road width extraction from aerial imagery

Leveraging Segment-Anything model for automated zero-shot ro...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Nan Xu Kerry Nice Sachith Seneviratne Mark Stevenson Department of Infrastructure Engineering Faculty of Engineering and IT Transport Health and Urban Systems Research Lab Melbourne School of Design Optimization and Pattern Recognition Group Faculty of Engineering and IT The University of Melbourne Parkville Victoria Australia

Segment-Anything model (SAM) is a foundation segmentation model published in April 2023. Trained on an unprecedented 11 million annotated images, the model can generate segmented masks bearing clear-cut contours by integrating user-provided prompts. It is zero-shot transferable, requiring no task-specific training. Nevertheless, its applicability for geographic vision tasks has not been fully evaluated. There is no automated prompt-feeding method incorporating with SAM that can work efficiently for purposeful batch processing as well. To fill these gaps, we developed a process that can be executed automatically from visual-prompts extraction to road width measurement, utilizing OpenStreetMap (OSM) and SAM. By examining the quality of segmentation in various image contexts, we evaluated the capacity and limitations of SAM working on aerial imagery. Through comparing measured widths to VicRoads records, we validated the specially designed width-measuring algorithm for high precision and accuracy. After this process, prompt-indicated zero-shot approach in solving basic geographic vision tasks is to be shaped synchronously on both theory and application ends.

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Performance Evaluation Of Machine Learning Algorithms For Rainfall Prediction Using Dimensionality Reduction Techniques

Performance Evaluation Of Machine Learning Algorithms For Ra...

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international conference on computing, Mathematics and Engineering Technologies (iCoMET)

作者： Sapna Kumari Muhammad Owais Raza Arsha Kumari Dept. of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Dept. of Information and Computing University of Sufism and Modern Sciences Bhitshah Pakistan Dept. of Electronics Engineering Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (数字)9798350335316

ISBN: (纸本)9798350335323

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. The main objective of this study is to evaluate a performance different Machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression.

关键词： Climate change Rain pattern recognition Predictive models Machine learning Neural networks

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13th EAI international conference on Simulation Tools and Techniques, SIMUtools 2021

13th EAI International Conference on Simulation Tools and Te...

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13th EAI international conference on Simulation Tools and Techniques, SIMUtools 2021

ISBN: (纸本)9783030971236

The proceedings contain 49 papers. The special focus in this conference is on Simulation Tools and Techniques. The topics include: A Video Parallel Retrieval Method Based on Deep Hash;cosmetics Sales Data Classification Method of Japanese Cross-Border E-Commerce Platform Based on Big Data;feature Filtering Spectral Clustering Method Based on High Dimensional Online Clustering Method;Research on CRM Boost PFC Converter Based on GaN Device;Application of Cascode GaN HEMT in LLC soft Switching Converter;Characteristics and Application of Cascode GaN HEMT;Simulation Research on Modular DC Grid-Connected PV System;key Quality Indicators of Social Networking Service;A Dynamic Migration Strategy of SDN Controllers in LEO Networks;discussion on the Project Teaching Method of Investment Banking in Local Ethnic Colleges and Universities;engineering Ecosystem Models: Semantics and Pragmatics;meta-modelling for Ecosystems Security;modelling Organizational Recovery;experimental Comparison of Open Source Discrete-Event Simulation Frameworks;Dynamic and Static Performance Analysis of SiC MOSFET with PWM Control;predictive Current Control of the Three-Level Four-Leg Active Filter Based on In-Phase Carrier Modulation;Harmonic Detection Method of Three-Phase Four-Wire APF Based on p-q-r Without Voltage-Sensor;Effective WiTech Identification Using Deep Transfer Learning with SNR as an Additional Feature;a Neural Network Algorithm of Learning Rate Adaptive Optimization and Its Application in Emitter recognition;a Network Traffic Measurement Approach for Edge computing Networks;agricultural Hyperspectral Image Classification Based on Deep Separable Convolutional Neural Networks;deep Reinforcement Learning Based Mimicry Defense System for IoT Message Transmission;research on Website Traffic Prediction Method Based on Deep Learning;Prediction Protein-Protein Interactions with LSTM;preface.

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