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Machine Learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication

Machine Learning for Wireless Network Optimization: Algorith...

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Advances in computer science, Electrical, Electronics, and Communication technologies (CE2CT), International Conference on

作者： Madhira Srinivas S. Surya Prachi Deepak Thakar Amit Kumar Jain Department of Computer Science & Engineering Data Science Geethanjali College of Engineering & Technology (GCET) Hyderabad India Sri Ramachandra Faculty of Engineering & Technology (SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai India Department of Computer Engineering KJ college of Engineering & Management Research Pune India Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function.

关键词： Machine learning algorithms Adaptive systems Wireless networks Scalability Machine learning Bandwidth Predictive models Prediction algorithms Resource management Optimization

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An innovation Proving of Knowledge Bases for Automated Reasoning for Information Analysis

An innovation Proving of Knowledge Bases for Automated Reaso...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sasibhushan Rao Chanthati Sachin S. Pund Suvasis Ghosh Bechoo Lal Ashish Kaushal Veeraswamy Ammisetty T. Rowe Price Harrisburg University of Science and Ttechnology USA Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of School of Management KIIT School of Management Bhubaneswar Department of Computer science and Engineering Konenru Lakshmaiah Education Foundation Vaddeswaram AP India Jindal Global Business School O P Jindal Global University Sonipat Haryana Department of Computer Science & Engineering college KoneruLakshmaiah Education Foundation Vaddeswaram

This paper examines the capability of using information Bases (KBs) in automatic reasoning for superior facts analysis. KBs, as a form of information representation, are uniquely suited for automated reasoning as they shop various kinds of domain-specific statistics in a single pool of declarative facts. The paper explores how to reliably and efficiently use such declarative data shops connecting one of a kind forms of artificial evidence and techniques. Right here, a new technique is proposed that unites the efficiency of RDF and the expressiveness of text-primarily based documents for representing the research context. An extension of the RDF format to represent reasoning steps and knowledge entities extra as it should be is proposed and tested on a repository of actual-global clinical and genetics datasets. The outcomes prove that the approach can improve computation of diverse components of studies workflow consisting of gene-disorder mapping tasks, whilst retaining affordable computational complexity. The proposed KB extends the capacity to generate efficient and powerful records analysis for research contexts and may result in new paradigms of automated reasoning.

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How an educational Robot Should Look Like: The Students' Perspective

How an Educational Robot Should Look Like: The Students' Per...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Kyprianou, Georgios Karousou, Alexandra Makris, Nikolaos Sarafis, Ilias Chatzichristofis, Savvas A. Amanatiadis, Angelos Neapolis University Pafos Intelligent Systems Lab Department of Computer Science Pafos Cyprus Democritus University of Thrace Department of Education Sciences in Early Childhood Alexandroupolis Greece Democritus University of Thrace Department of Primary Education Alexandroupolis Greece International Hellenic University Department of Chemistry Kavala Greece Democritus University of Thrace Department of Production Management Engineering Xanthi Greece

ISBN: (纸本)9781665441544

In the last decade, there is a growing demand for educational robots in consumer electronics. This can be observed from the high promotion educational robots get from crowd-funding platforms, where more than 2000 projects have been funded successfully. Lego, Robolink, Hanson Robotics, Modular Robotics, and Primo Toys are only a few companies that disrupt education. Using robotic platforms like these, students now learn through experiences and structure their knowledge under the Constructivist approaches that modern education promotes. The teacher is not the leading actor anymore but more like a guide on the students' path on learning development. The robotic platform is now a vital aspect of the students' development. Thus, huge research is being undertaken on each company's construction, characteristics, and morphology to give to their product. This should not come in random, but on the contrary should be examined under the requirements of the ones they will use it, the students/children. The main objective of this paper is to investigate the characteristics a robot should have and the way of interaction and socialization with it, as the students are seeking and expecting it. This could provide the attributes and standards of constructing the perfect model for the students to acquire what they are pursuing in a more effective, exciting manner without losing their motivation. © 2022 IEEE.

关键词： educational robots

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Implementing AI for Enhanced Public Services ***: A Methodology for the Brazilian Federal Government 20

Implementing AI for Enhanced Public Services ***: A Methodol...

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20th International Conference on Web Information Systems and technologies, WEBIST 2024

作者： Kely De Melo, Maísa Araújo Dos Reis, Silvia Di Oliveira, Vinícius Victor Almeida Faria, Allan De Lima, Ricardo Weigang, Li Francisco Salm, Jose Gabriel De Moraes Souza, Joao Freitas, Vérica Carvalho Brom, Pedro Kimura, Herbert Oliveira Cajueiro, Daniel Luiz Da Silva, Gladston Celestino, Victor Rafael R. LAMFO - Lab. of ML in Finance and Organizations University of Brasília Campus Darcy Ribeiro Brasília Brazil TransLab Department of Computer Science University of Brasília Campus Darcy Ribeiro Brasília Brazil Ministry of Management and Innovation in Public Services Federal District Brazil Department of Statistics University of Brasília Campus Darcy Ribeiro Brasília Brazil Department of Economics University of Brasília Campus Darcy Ribeiro Brasília Brazil Department of Mathematics Federal Institute of Education Science and Technology of Minas Gerais Formiga Brazil Department of Business Administration University of Brasília Campus Darcy Ribeiro Brasília Brazil School of Business and Management Uberlandia Federal University Uberlândia Brazil Department of Economics and Business Administration Brazilian Institute of Education Development and Research - IDP Brasília Brazil Department of Mathematics Federal Institute of Education Science and Technology of Brasília Campus Estrutural Brasília Brazil Federal District Secretariat of Economy Brasília Brazil Department of Computer Science University of Brasília Campus Darcy Ribeiro Brasília Brazil University of the State of Santa Catarina Santa Catarina Florianópolis Brazil

ISBN: (纸本)9789897587184

The website portal of the Brazilian federal government (***) consists of pages from almost 40 ministries, 180 public agencies and up to 5000 public services for all citizens, posing a significant challenge in improving service quality. This article presents an innovative methodology to implement artificial intelligence (AI) to address these challenges, to enhance the efficiency, accessibility, and quality of services to the population. The methodology combines elements of Lean Office, Design Sprint, Analytic Hierarchy Process (AHP), and advanced AI techniques, particularly Large Language Models (LLMs), making it flexible and adaptable to the needs of government entities. Developed in collaboration with project managers, public servants, and stakeholders, the methodology includes a survey of demands, selection, and prototyping of AI projects in a complex government context. The practical application selected the *** portal for prototyping, involving the development of an advanced generative agent to interact with citizens, clarify doubts, direct to the requested services, and provide human interaction when necessary. The recommended practices offer a valuable contribution to other developing countries seeking to integrate AI solutions into their public services. Copyright © 2024 by SCITEPRESS.

关键词： Analytic hierarchy process

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Enhancement of Vendor-Managed Inventory Planning Through Deep Reinforcement Learning

Enhancement of Vendor-Managed Inventory Planning Through Dee...

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Simulation Winter Conference

作者： Marco Ratusny Jee Hyung Kim Hajime Sekiya Maximilian Schiffer Hans Ehm Infineon Technologies AG Neubiberg BAVARIA Germany Department of Mathematics and Computer Science University of Hagen Hagen NORTH RHINE-WESTPHALIA Germany School of Management Technical University Munich Munich BAVARIA Germany

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

We explore the application of Twin Delayed Deep Deterministic Policy Gradient (TD3), a Deep Reinforcement Learning (DRL) algorithm, for optimizing Vendor-Managed Inventory (VMI) systems in the semiconductor industry. We introduce a novel multi-scenario DRL algorithm with a continuous action space, designed to effectively manage diverse product/customer combinations, thereby improving VMI performance. We evaluate our algorithm's efficacy on three distinct products as well as 100 product/customer combinations for the multi-scenario approach. A sensitivity analysis examines the effects of varying shipment penalties on the percentage of no violations (PNV) and shipments. Our findings indicate that our DRL-based VMI model significantly surpasses existing policies used in the semiconductor industry by five percentage points.

关键词： Semiconductor device modeling Sensitivity analysis Electronics industry Semiconductor device manufacture Deep reinforcement learning Planning

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Smart Modular Few-Shot Learning Framework Changes Classification of Signal Modulation

Smart Modular Few-Shot Learning Framework Changes Classifica...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Spoorthi, T. Tabassum, Husna Devika, S.V. Rao, K. B. V. Brahma Ramachandra, A.C. Gst Gitam University Bengaluru Campus Dept. of Cse Bengaluru India Hkbk College of Engineering Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The purpose of this research is to categorise signal modulation using few-shot learning. The first approach is an intelligent, modular, few-shot learning framework. This system's two components are IQF and 1D-SFP, and it also has a task-driven network design. The 1D-SFP module is responsible for obtaining high-level characteristics from the raw signal, while the IQF module is in charge of retrieving low-level information. The number of 1D-SFP modules required for processing a large amount of data is calculated by studying the correlation in the hidden layer. We evaluate the suggested method using a publicly available dataset and show that it beats standard strategies in terms of accuracy. It can also learn from a small number of instances and is adaptable enough to handle innovative modulation schemes. This enables it to be used in wireless networks for data transmission and reception. The second approach uses a graph convolutional network (GCN) to calculate how many 1D-SFP modules should be integrated into a neural network architecture for a particular job. On benchmark datasets, the presented frameworks outperformed a range of state-of-the-art, conventional methodologies. Both approaches perform preprocessing, representation learning, and classification using their own distinct algorithmic frameworks. The presented strategies have the potential to increase signal modulation classification precision and efficiency in cognitive radios and other kinds of wireless communication. These gains are possible because of the potential of these techniques. © 2023 IEEE.

关键词： Learning systems

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Optimizing Wireless Power Transfers for Telecommunication Networks: Performance Evaluation and Strategies

Optimizing Wireless Power Transfers for Telecommunication Ne...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Upendra Mohan Bhatt Somya Sharma G. Arun Francis Neeraj Kaushik S. Hemelatha Y. K. Sharma Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Commerce and Management IIMT University Meerut Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Department of ECE TMU Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Computer Engineering Vishwakarma Institute of Information Technology Pune

This study examines the typical performance of a wirelessly fueled transceiver in which an electricity source interacts with a target while being supplied by a specialized power transmitter (PB). As a transmission device, it is believed that the PB is competent of frequency prediction, cloud based spatial multiplexing, and spectral detection. When using a night before going to bed technique, the provider first collects information from either a PB with numerous receivers before sending data to the target. Theoretical formulas for the annualized pace for two distinct transmit antennas, particularly regarding postpone accepting and postponement incompatible, are obtained under the assumption of Nakagami-m path loss. Additionally, in some specific situations, such as the high send wattage domain and a huge assortment of antennas, closed-form methods for the ideal time spread that optimize the typical transmission are found. The effect of co-channel crosstalk is then researched. The total output can be increased at the PB by adding more broadcast transmitters, and since interruption can be used as an additional energy source, it may even be possible to use interruption to your advantage. This has been demonstrated by analytical and modeling findings. Additionally, the cruising pace is not significantly impacted by the energy conversion link's waning intensity level, particularly if there are a lot of PB receivers. Lastly, it is noted that the average flow is significantly influenced by the source.

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Blockchain-based Privacy-Preserving System for Internet of Things (IoT)

Blockchain-based Privacy-Preserving System for Internet of T...

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International Conference on Communication and Electronics Systems (ICCES)

作者： C. N. Ravi S Dinesh Krishnan Manikannan Kaliyaperumal Sumita Kumar A. V. S. Ram Prasad S. Suma Christal Mary Computer Science & Engineering Department CMR Engineering College Hyderabad Telangana Department of Computer Science & Engineering B V Raju Institute of Technology Narsapur Computer Science & Engineering R.M.K Engineering College R.S.M Nagar Department of Engineering and Technologies Computer Science & Engineering Bharati Vidyapeeth Deemed to be University Navi Mumbai Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu

The shortcomings of conventional healthcare systems are becoming more apparent as the Internet of Things (IoT) becomes ever more pervasive in people's daily lives. The standard procedure for dealing with confidential data frequently results in unwanted public disclosure. Blockchain's agreements make it possible to automate the safe transfer of patient data based on their individual authorization settings. To create a completely untraceable trading platform, blockchain technology is frequently implemented. To protect user data in the Internet of Things (IoT), a privacy-protecting protocol is developed using the blockchain. It is important to consider that the rogue cloud server might potentially develop a database of user behaviour profiles, which could be a serious breach of privacy. Confidentiality makes it more difficult to resolve disagreements about private data exchange transactions. When users of metadata are falsely accused, their rights are difficult to defend. Also, smart contracts are used to enforce security protocols and data privacy for multisharing adaptable access control. There is no reason to doubt the safety of the proposed work. The proposed work is efficient and applicable, as demonstrated by the efficiency evaluation and experimental findings.

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Facial Expression Recognition Based on Pruning Optimization Technology

Facial Expression Recognition Based on Pruning Optimization ...

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作者： Chengyu Jia Xinyi Li Rui Qian Huayu Sun Department of Computer Science Singapore Institute of Management (SIM) College of Media Engineering Communication University of Zhejiang School of oversea education Changzhou University Materials Science and Engineering Northeastern University

Facial expression recognition is based on deep learning, which has become one of the main topics in artificial intelligence. Nevertheless, the complex network structure causes numerous parameter redundancy, which leads to low recognition efficiency. Therefore, researching how to reduce parameter redundancy makes sense. In addition, today's machine expression recognition has some limitations and constraints, while the generalization ability is poor. Aiming at addressing the problems that excessive parameter redundancy brings, for example, low efficiency and massive limitations. This paper will put forward a facial expression recognition technology that is based on pruning optimization to improve existing models. Based on VGGNet, ResNet and SENet, these three convolutional neural networks modify the convolution layer of the network, then add the attention module and pruning optimization. Through face detection, face area feature representation,expression recognition, and other steps. Realize the recognition of multiple facial expressions. The CK+ dataset and JAFFE dataset are independently divided into 4/5 for training and 1/5 for *** results show that after inserting CBAM, except ResNet18, the accuracy of the other two networks all improved, SENet18 improved the most significantly. After pruning, the accuracy and speed of VGG16 and ResNet18 improved, but the accuracy of SENet18 decreased. In summary,the optimized model has good applicability and generalization ability.

关键词： Network Sliming image recognition VGGNet ResNet SENet CBAM

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ARGai: AI Fusion Approaches for Classification of S. aureus Strains and Identification of Antibiotic Resistant Genes

ARGai: AI Fusion Approaches for Classification of S. aureus ...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Debasish Swapnesh Kumar Nayak Archana Dash Sweta Padma Routray Santanu Sahoo Tripti Swarnkar Department of Computer Science and Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Department of Computer Science and Engineering SOET Centurion University of Technology and Management Bhubaneswar India Centre of Biotechnology Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of ECE Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Application National Institute of Technology Raipur India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Staphylococcus aureus (S. aureus) causes bone and joint infections, pneumonia, and bloodstream infections, the most common and fatal of these illnesses. Rapid identification of resistant strains and the antibiotic resistance genes (ARGs) linked to them can enhance the diagnostic process for various infectious illnesses. We introduce a new clinical support system that uses a hybrid multi-stage significant gene identification technique called MRMR-LASSO to select resistant and significant genes from next-generation sequencing (NGS) datasets. It is necessary to identify the S. aureus resistance strain from among thousands of relevant genes in NGS datasets, which are typically complex and comprise many genes with few samples. The curse of dimensionality makes it hard to pick informative genes from many genes. Optimization techniques are employed to resolve the gene selection issue. To select important genes for our ARGai model, we employed a filter in conjunction with an embedding approach. With its combination of machine learning (ML) and deep learning (DL) methods, ARGai can efficiently and accurately identify the important genes in S. aureus with a 96% success rate. Examining the biological relevance of chosen gene activity guarantees that ARGai's classification performance on susceptible and resistant strains is outstanding. Several cutting-edge AI models used for gene selection and strain classification are compared to our ARGai model. By shortening the screening period, this proposed ARGai may aid in subsequent phases of illness treatment, such as disease diagnosis and medication design.

关键词： Sequential analysis Pneumonia Biological system modeling Antibiotics Medical diagnosis Next generation networking Optimization Medical diagnostic imaging Strain Immune system

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