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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Dimitrije Stojanović Nenad Četić Jelena Kocić Bogdan Pavković Departmant of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia TTTech Auto Customer Programs Belgrade Serbia

Autonomous driving systems rely on accurate and reliable lane detection to safely navigate roads. In this paper, we propose a method for improving lane annotation in autonomous driving data sets using classical computer vision techniques. The proposed method combines the Hough transform and linear curve fitting to detect and smooth the positions of lane markings in a video stream. We evaluate the performance of the proposed method on the Berkeley DeepDrive (BDD) dataset and compare it to the ground truth annotations. Results show that the proposed method achieves a high level of accuracy and robustness in lane detection, and can effectively improve lane annotation in autonomous driving data sets. Our method provides a valuable tool for training and evaluating autonomous driving systems, and can also be applied to improve annotation in different datasets.

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Analysis of hyperbolic reflections amplitudes in GPR data

Analysis of hyperbolic reflections amplitudes in GPR data

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Ž eljko Bugarinović Aleksandar Ristić Milan Vrtunski Miro Govedarica Department for computing and control engineering Faculty of technical sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9781665469487

ISBN: (纸本)9781665469494

In this paper an analysis of the amplitude of the reflected signal in extracted points on hyperbolic reflections from radargram is presented. Currently, many papers deal with the automated search for hyperbola peak in radargram. On the other hand, according to the authors' best knowledge, there is an insufficient number of papers related to the extraction of points on hyperbolas prongs, even though they can be used to determine additional information, such as the radius of detected pipe or cable. In the first part of this paper a procedure for extraction of coordinates of points on hyperbolic reflections prongs. Second part of the paper contains more detailed analysis of the value of the reflected signal of extracted points in relation to different values of acquisition parameters. This analysis has two goals: (1) to conclude which parameter has the biggest influence to amplitude and (2) to determine whether an amplitude based stopping criteria for point extraction can be established.

关键词： Dielectric constant Program processors Signal processing algorithms Signal processing Market research Reflection Real-time systems

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Impact of Parameters for the In-situ Measurement of the Electric Field Strength of the SSB Signal in a 5G NR System Using the Frequency-Selective Method in Zero-Span Mode

Impact of Parameters for the In-situ Measurement of the Elec...

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International Conference on Smart Systems and Technologies (SST)

作者： Vanja Mandrić Slavko Rupčić Bogdan Pavković Josip Ilakovac Department of Communication Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Measuring 5G systems is a major challenge for measurement engineers, who need to be aware of all the limitations of the new generation The parameters of the frequency-selective measurement system (spectrum analyzer - ROHDE & SCHWARZ FSH8) were investigated in the measurement of 5G NR SSB signals (3.4 – 3.8 GHz). The focus was on the setting parameters which, according to the literature, have an influence on the measurement: RBW, VBW, SWEEP TIME and the duration of the measurement. All measurements were carried out in zero-span mode. In the second part of the measurement, these SSB synchronization signal measurement results were compared with the 5G signal measurement results at the same location using the NARDA SRM-3006 device equipped with a decoder for the 5G NR system (FR1) to determine the optimal method for measuring these signals with a spectrum analyzer (SA).The measurement results show that the resolution bandwidth (RBW) has the largest variation in the measured electric field (EF) strength values of the SSB 5G NR signal of 17.20 dB (MH/RMS) and 17.70 (RMS/AGV10). It is therefore necessary to pay particular attention to the setting of this measurement parameter. In addition, the video resolution (VBW) shows a significantly lower field strength variation of 2.50 dB, but also the same deviation from the reference value of 2.50 dB (RMS/AGV10). Other analyzed parameters have a negligible influence on the measurement of the EF *** post-processing was performed in the work to determine the final (maximum) value of the EF strength at the points of assumed highest exposure, which is used to assess the impact and compliance with the permissible values according to the relevant standards.

关键词： Time-frequency analysis 5G mobile communication Measurement uncertainty Electric variables measurement Amplitude modulation Time measurement Frequency measurement Decoding Electric fields Signal resolution

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Anomaly Detection in Hotel Reviews: Applying Data Science for Enhanced Review Integrity

Anomaly Detection in Hotel Reviews: Applying Data Science fo...

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Telecommunications Forum (TELFOR)

作者： Milena Nikolić Miloš Stojanović Marina Marjanović Department of Information and Communication Technologies The Academy of Applied Technical and Preschool Studies Niš Serbia Faculty of Technical Sciences Engineering Management and Electrical Engineering and Computing Singidunum University Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

As the hotel industry increasingly relies on online reviews to attract and retain customers, distinguishing between genuine and fraudulent feedback has become critical. This paper presents a methodology for anomaly detection in hotel reviews by employing advanced exploratory data analysis (EDA). Key aspects of our analysis include evaluating sentiment and review similarity, uncovering unusual behavioral patterns, and identifying patterns in temporal posting trends and distributions. Time series analysis helps to recognize unusual spikes and shifts in user review activity. Additionally, we address issues such as misleading titles and ratings by tracing correlations among crucial review elements. This methodology provides actionable insights and recommendations for improving review filtering processes and addressing fraudulent content, offering a comprehensive analysis of online review patterns without relying on predictive modeling.

关键词： Industries Sentiment analysis Correlation Reviews Time series analysis Predictive models Market research Telecommunications Reliability Anomaly detection

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Integrating Cloud computing and Naive Bayes for Precision Detection and Classification of Sheet and Rill Erosion in Agriculture 2

Integrating Cloud Computing and Naive Bayes for Precision De...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Deshpande, Ashish Govindrao Raman, Ramakrishnan Vekariya, Vipul Mishra, Nilamadhab Arumugam, Sundaram Srinivasan, C. Pune Maharashtra Pune India Symbiosis Institute of Business Management Pune Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Navajo Technical University Department of Electrical Engineering Crown pointNM United States Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Sustainable agriculture has significant challenges from sheet and rill erosion for effective techniques. A new way to accurately identify and classifier sheet and rill erosion in agricultural landscapes with the combination of cloud computing and Naïve Bayes. To differentiate between these erosion patterns work proposes a technique that uses ground truth data and high-resolution satellite images. The technique gets results by using the Naïve Bayes algorithm, which is famous for being simple and successful in classification problems. More efficient and accurate and advanced methods by conducting comprehensive experiments and validations. This technology takes use of the scalability and accessibility of cloud computing to conduct erosion risk assessments in real-time, providing agricultural stakeholders with valuable information to enhance soil conservation initiatives. It highlights the revolutionary power of new technology to improve agricultural land management methods and address environmental issues. Sustainable agricultural practices mitigate the adverse implications of soil erosion by offering exact detection and classification of erosion events, which lead to better informed decision-making. © 2024 IEEE.

关键词： Environmental Monitoring Erosion Risk Assessment Ground Truth Data Land Management Satellite Imagery Soil Conservation Sustainable Agriculture

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CVS-FLN: a novel IoT-IDS model based on metaheuristic feature selection and neural network classification model

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Multimedia Tools and Applications 2024年第39期83卷 86557-86591页

作者： Geetha, R. Jegatheesan, A. Dhanaraj, Rajesh Kumar Vijayalakshmi, K. Nayyar, Anand Arulkumar, V. Velmurugan, J. Thavasimuthu, Rajendran Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Pune India Department of Computer Science and Engineering Ramco Institute of Technology Tamil Nadu Rajapalayam India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The Internet of Things (IoT) is one of the technologies that will be used all over the world in the future, and its security and privacy features are the primary concerns. However, the most critical limitation to overcome before the IoT's widespread use is addressing its security concerns. One of the most critical tasks to address the security problems posed by the IoT is the detection of network intrusions. Intrusion Detection Systems (IDS) for the IoT face substantial challenges because of the functional and physical diversity of the devices. In the rapidly growing IoT industry, the use of IDS is essential for ensuring security since many devices communicate efficiently. IDS are crucial for continual monitoring and responding to any security risks, ensuring the integrity and reliability of interconnected IoT networks. The primary objective of this research is to develop a feature-selection-based intrusion classification model. Therefore, we develop an IoT-IDS feature selection-based classification model to detect intrusions. We propose a metaheuristic algorithm, the Chaotic Vortex Search (CVS) algorithm, for feature selection. The Fast-Learning Network (FLN), an artificial neural network (ANN) model, is additionally proposed for classification. We use the IDS datasets, such as CIC IDS-2017 and BoT-IoT, to evaluate our research model. To test and validate the proposed model, extensive experiments were performed, and the outcomes stated that the CVS-FLN model achieved 99.77% accuracy, 99.92% specificity, 99.60% precision, 99.81% detection rate, and a 99.72% F1-score in the CIC IDS dataset, and 99.68% accuracy, 99.30% precision, 99.83% specificity, 99.11% detection rate, and a 99.21% F1-score in evaluating the BoT-IoT dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Predictive Diagnostics of Power Generating Equipment Failures Based on Neural Network Technology 5

Predictive Diagnostics of Power Generating Equipment Failure...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nekrasov, Ivan V. Konstantinovskij, Yurij D. Kukin, Nikolay S. V.A. Trapeznikov Institute of Control Sciences Ras Laboratory of Schedule Theory and Discrete Optimization Moscow Russia Bauman Moscow State Technical University Aerospace Faculty Moscow Russia Autonomous Non-Profit Organization 'Institute of Engineering Physics' Serpukhov Russia

ISBN: (纸本)9798350363739

This paper is covers basic problems of creating a predictive diagnostic system for assets and units of a heating power plant based on a fully connected neural network. The research confirms frequent situation when the network cannot be properly trained against typical failures due to lack of statistical data associated with each type of failure. In order to solve the problem we implement the idea of utilizing additional data that belongs to pre-fault time period and/or corresponds to warning alarms thrown before the failure itself. The primary analysis shows that existing databases from industrial enterprises contain significant amount of pre-fault data. We introduce a procedure of creating and preprocessing the training set based on pre-fault data measurements. The procedure includes basic stages of detecting cross-correlation between measured parameters, defining each parameter variation/range and choosing the data most applicable for neural network training. The result of training the network and testing the diagnostic system on real data taken from the turbine's feeding pump located at the heat power plant. Based on the real result demonstrated we certify positive outcome of the designed predictive diagnostic system and share some future improvements to be done. © 2024 IEEE.

关键词： Machine learning

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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Computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Hybrid Cuckoo Search with Fuzzy Algorithm to Improve the Wireless Network System 2

Hybrid Cuckoo Search with Fuzzy Algorithm to Improve the Wir...

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2nd International Conference on Microwave, Antenna and Communication, MAC 2024

作者： Priya, C. Ranganathan, Chitra Sabapathy Vetriselvi, D. Vanakamamidi, Rama Krishna Dhanalakshmi, S. Srinivasan, C. Department of Electronics and Communication Engineering Andhra Pradesh Puttur India Mphasis Corporation Tamil Nadu Chennai India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India HCL Technology Ltd Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350104

Effective connection quality is the basis of wireless network topology management and routing control. Effective link quality estimates may increase throughput and assure data transfer, extending the whole network's lifespan. Quality-of-service (QoS) routing is crucial in wireless networks to guarantee that data to be sent along a trusted channel and address the issue of energy balancing. This paper presents a Hybrid Cuckoo Search with a Fuzzy (HCSF) algorithm to improve the wireless network system. This mechanism uses the cuckoo search (CS) algorithm to select the neighbor nodes intelligently;as a result, it minimizes the routing overhead. Furthermore, it uses the fuzzy logic system to select the best forwarder from sender to receiver. Hence, it minimizes the network delay and packet loss ratio. The experimental outcomes using the Network Simulator tool significantly enhance the forward ratio and minimize the wireless network's routing overhead. © 2024 IEEE.

关键词： Quality of service

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