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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Indarjit, Elisha Balyan, Vipin Adonis, Marco Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town Bellville South Africa

ISBN: (纸本)9781032874036

The increase of fraudsters and fraud attacks on the communication network plays a major role in the loss of revenue, network abuse, and degradation of services. Communication and cloud networks belong to separate companies and consist of handover points from one network to another. The scope is to bring communication and cloud networks closer and understand network traffic profiles using the Software-Defined Networking (SDN) concept. The SDN controller will serve to route the extracted tapped data to a central server instead of making use of the live network to route traffic. To apply policies to the traffic and identify which user traffic is a fraud case, and further to send a block signal to the network charging element on the communication network. The flow process will be automated, and to protect the network from fraud attacks, using the SDN controller in a seamless manner and maintaining the performance of the network, The journal article explores traditional fraud detection techniques and will define a new framework for communication and cloud providers to enable the detection and blocking of fraud. The research solution will propose three network architectures for the identification of fraud and integration within communication and cloud networks. The aim of the study is to perform real-time data classification, reporting of fraudulent subscribers, applications, and protocols;real-time analytics, and real-time blocking of data that is classified as fraudulent activity within the mobile data network. The main purpose is to achieve a higher energy/service fraud detection rate than similar work in the field, to provide a solution that integrates within the traditional network, and to further block fraud within the network. © 2025 the Author(s).

关键词： cloud network communication network detection fraud software-defined network

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Improved Quasi-Random Designs with Constant Pool Sizes for Group Testing 18

Improved Quasi-Random Designs with Constant Pool Sizes for G...

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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Hamada, Ryouji Lu, Xiao-Nan Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering Chofu-shi Tokyo Japan Gifu University Faculty of Engineering Department of Electrical Electronics and Computer Engineering Gifu-shi Gifu Japan

ISBN: (纸本)9784885523519

Group testing is a well-studied approach for identifying defective items among a large amount of items by conducting a relatively small number of tests on pools of items. In this paper, we propose a novel method for generating quasi-random designs with constant pool sizes for noiseless non-adaptive group testing. Our approach incorporates insights from combinatorial properties to optimize the distribution of Hamming distances in pooling designs. Simulations with established practical decoding algorithms show that our quasi-random designs outperform existing random and quasi-random methods in both identification accuracy and stability. These improved designs have the potential to provide significant benefits for real-world applications of group testing, such as PCR testing in large populations and network security. © 2024 The Institute of electronics, Information and Communication Engineers.

关键词： Hamming distance

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HsMM-Based Mobility Aware Cell Association Method for Dynamic Bandwidth Management in 5G-FRANs 5th

HsMM-Based Mobility Aware Cell Association Method for Dynam...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Pana, Vuyo Babalola, Oluwaseyi P. Balyan, Vipin Department of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Bellville7535 South Africa

ISBN: (纸本)9789819725496

In this paper, a cell association method for proactively managing bandwidth in 5G fog radio access networks 5G FRANs) by considering user mobility is developed. The proposed method jointly utilises cell identifications (cell-IDs) information and distributions of sojourn time to develop a hidden semi-Markov model (HsMM) for predicting the most appropriate next cell that satisfies downlink rate requirements. Furthermore, the cell that offers the optimal rate is determined based on an optimization solution. The proposed HsMM considers ping-pong effects, missing values, and limited context from wireless networks to understand user mobility patterns. The result shows that the HsMM-based scheme yields a high accuracy performance according to the number of observation time intervals. Also, the proposed scheme produces an improved user satisfactory percentage compared to the existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bandwidth

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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PV and Battery Sizing and Cost Study for Small Remote Islands Considering Load Demand Peak Shift 13

PV and Battery Sizing and Cost Study for Small Remote Island...

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13th International Conference on Power, Energy and electrical engineering, CPEEE 2023

作者： Kitalong, Clarence Noro, Yasuhiro Electrical Engineering and Electronics Program Kogakuin University Tokyo Japan Kogakuin University Department of Electrical and Electronic Engineering Tokyo Japan

ISBN: (纸本)9781665490146

This paper discusses energy and cost comparison for 9 different combinations of Photovoltaic (PV) and Lithium-Ion Battery Energy Storage System (BESS) sizes with load demand peak changes for the Kayangel Power System (KPS). 9 PV and BESS combinations were modeled with the existing 90kW Diesel Generator (DG) and simulated while analyzing the kWh energy values of DG operation, PV curtailment and battery power flow energy values. A comparative cost analysis was then conducted on these 3 energies using Installation Costs (IC), diesel fuel costs and energy charge costs. Following this, the study was expanded to a 20-year time period for more complete system lifetime cost estimations. Simulated energy losses and system costs showed mostly favorable values during increased PV and BESS sizes and shifted load demand peak. Simulation results also identified combinations where increased sizing and shifted load demand peak made little difference. © 2023 IEEE.

关键词： Energy dissipation

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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An Advanced IoT and GPS-GSM Based Real-Time Automated Data Monitoring Robot with Image Processing and Live Streaming for Human Life and Land Mine Detection Systems

An Advanced IoT and GPS-GSM Based Real-Time Automated Data M...

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2024 International Conference on computer, electrical and Communication engineering, ICCECE 2024

作者： Islam, Md. Muhaiminul Akter, Farzana Hossain, Md. Rakib Faruk Hossain, Md. Mahedy Hasan, S.M. Yakin Srizon, Md. Azmain Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350386479

An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time communication between complicated human-machine interfaces is one of the difficulties of the fourth industrial revolution. That is why robots are essential in this field because they improve human lifestyles, lighten workloads, and carry out several dangerous tasks. This research presents a groundbreaking advancement in the form of an automated real-time IoT-based data monitoring robot in an era characterized by the proliferation of automation and the Internet of Things (IoT). This ground-breaking system combines GPS-GSM functionality, human life detection, land mine detection, image processing, and live streaming in an efficient manner. The robot utilizes its IoT framework and a variety of embedded sensors and technology to gather, process, and transmit realtime data from its surroundings. Notably, it supports disaster response, military operations, and safety measures by performing the vital tasks of detecting human life and land mines. The ability to categorize and process visual data is improved by the image processing feature, and situational awareness is provided by live streaming © 2024 IEEE.

关键词： Global positioning system

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Enhancing IoT Networking Through Predictive Big Data Processing 2

Enhancing IoT Networking Through Predictive Big Data Process...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jain, Sandeep Kumar Hannah Jessie Rani, R. Paikaray, Divya Vivekananda Global University Department of Electrical Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

the net of things (IoT) promises to revolutionize the way humans interact with their surroundings. Notwithstanding this promise, the deployment of IoT networks is hindered by using several challenges, particularly related to communication, scalability, and electricity performance. This paper proposes a technique to address those demanding situations through predictive big statistics processing blended with the use of new network technology. In particular, the use of prediction fashions, system studying, and analytics algorithms will permit IoT networks to investigate past and present facts in order to forecast destiny traits. Additionally, the use of latest and existing network technologies, which include extremely-low strength mesh networks, 5G, and electricity-conscious network routing protocols will help to improve the overall performance of IoT networks. In conclusion, this paper gives a method to decorate the characteristic of IoT networks through predictive big statistics processing as a way to permit more effective, efficient, and reliable communique for all related devices. © 2024 IEEE.

关键词： Scalability

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Smart IoT_Cloud-Based ML Technique to Classify Breast Cancer 2

Smart IoT_Cloud-Based ML Technique to Classify Breast Cancer

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Prusty, Sashikanta Dash, Sujit Kumar Prusty, Sushree Gayatri Priyadarsini Dalai, Sasanka Sekhar Tripathy, Nrusingha Nayak, Subrat Kumar Department of Computer Science & Engineering Bhubaneswar751030 India Department of Electrical & Electronics Engineering Bhubaneswar751030 India

ISBN: (纸本)9798350344882

IoT and cloud computing have a significant impact on healthcare services, particularly when it comes to disease prediction in smart cities. Big data about breast cancer (BC) can be sent through IoT devices (digital sensors, etc.) and stored in the cloud. As a result, these huge data are used to improve BC prediction accuracy in a cloud setting. Thus, BC-based cloud IoT is thought to be a significant issue that smart city stakeholders in health care must deal with. In smart cities, cloud computing helps patients predict the presence of BC anywhere and at any time by using advanced machine learning (ML) techniques. The main goal of this study is to demonstrate the effectiveness of a secure Internet of Things and cloud computing in the identification of BC at our home. Thus, we proposed an ICM (IoT_Cloud-based ML) method that describes the workflow of how a user can predict BC disease at any place and at any time. And finally, we designed performance metrics for all three models and found logistic regression model (LR) outperforms with higher accuracy of 83% than DT at 72% and NB at 80%. However, all these experiments have been carried out using Python 3.8.3 programming software on the Jupyter 6.4.3 Notebook platform in Windows 10 operating system. © 2023 IEEE.

关键词： Cloud computing

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