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Application of IoT in the study of biogas release parameters 33

Application of IoT in the study of biogas release parameters

引用

33rd International Scientific Conference Electronics, ET 2024

作者： Nikolov, Neven K. Tzanova, Anelia I. Koparanov, Kiril A. Antonova, Elena V. Minkovska, Daniela V. Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Chemistry 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350376449

This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are performed. Two types of samples were tested, the obtained results were displayed graphically, and the data was analyzed. © 2024 IEEE.

关键词： biogas ESP32 IoT measurement MQTT

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Hassenstein-Reichardt Detector Using Controllable Single Pulse Time-Delay Circuit for Neuromorphic Hardware 11

Hassenstein-Reichardt Detector Using Controllable Single Pul...

引用

11th International Scientific Conference on computer Science, COMSCI 2023

作者： Trendafilov, Ianislav Gancheva, Veska Neuromorphica Ltd. Sofia1335 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

Inspired by the visual system of the fruit fly, we had created a generic building block for neuromorphic hardware that is vital for third generation neural networks. This enables time delay to be parametrized in a cheap and inexpensive way. The advantage of our proposal is the possibility of building and testing new networks using dynamicist approach in artificial intelligence. © 2023 IEEE.

关键词： Neural networks

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Neuromorphic Assisted Sensor Grids 32

Neuromorphic Assisted Sensor Grids

引用

32nd International Scientific Conference Electronics, ET 2023

ISBN: (纸本)9798350302004

A particular problem in using artificial intelligence techniques in the sensor grid is the high power consumption. Remote sensors are usually limited by the amount of power available, thus our general goal is to minimize it while preserving accurate experimentation results. Using emerging technologies like spiking neural networks and neuromorphic hardware, we can create sensor grids that perform data processing with preserving low power consumption. In this paper, we demonstrate a remote node with integrated visual sensor that works on less than 10mW of energy, while performing continuous scene monitoring, object detection and classification. The system has intelligent power management and the ability to send data wirelessly. © 2023 IEEE.

关键词： Object detection

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METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALGORITHM 5

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALG...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Zozulia, Vladislav Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350384499

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models. ©2024 IEEE.

关键词： Network intrusion

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Software Anomaly Detection Method Based on Artificial Neural Network

Software Anomaly Detection Method Based on Artificial Neural...

引用

IEEE International Conference on e-Business Engineering (ICEBE)

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

A growing range of data sets have been created in recent years; these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. As a result, network security in data transmission is becoming a more important area of study. One well-known method for safeguarding computer systems is the deployment of intrusion detection systems. This study proposes an artificial intelligence based method for data analysis-based anomaly detection. Methods based on machine learning and rules are mixed together. The right rules are created via a genetic algorithm. Relevant features are extracted using principal component analysis with the goal of enhancing performance. The KDD Cup 1999 dataset is used to empirically validate the suggested procedure, satisfying the criterion of using appropriate data. Using the well-known benchmark dataset, the suggested approach is used to identify and examine four different kinds of attacks: Neptune, Ipsweep, Pod, and Teardrop. During the machine learning phase, the data is categorized into categories of attacks and normal behavior after the features set during the training phase are tested. For the purpose of data analysis, the input data is divided into training and testing sets for an artificial neural network. The first 80% of the data are used to train the neural network, and the remaining 20% are used for testing. The estimated accuracy improves with the number of epochs and is higher for training data and lower for validation test data, according to experimental results. Consequently, the trained model can be retained and used to detect discrepancies. The learnt model is used to the system to compute new input parameters that are not used during training or validation.

关键词： Training Accuracy Predictive models Network security Feature extraction Data structures Data models Software Anomaly detection Principal component analysis

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Healthcare Data Analytics Based on Machine Learning

Healthcare Data Analytics Based on Machine Learning

引用

Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they enable the identification and creation of models of complicated datasets and the interactions between them. This, in turn, enables the successful prediction of future outcomes associated with a specific sickness type. An artificial intelligence-based approach to healthcare data analytics is presented, which leverages data to build a desired model and solve a particular issue. The suggested approach for healthcare data analytics uses a random forest and feedforward artificial neural network with two hidden layers as its basis to get the best model.

关键词： Analytical models Data analysis Computational modeling Medical services Artificial neural networks Learning (artificial intelligence) Data models Telecommunication computing Informatics Random forests

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Parallel Multithreaded Filtering of X-Ray Computed Tomography Images

Parallel Multithreaded Filtering of X-Ray Computed Tomograph...

引用

Computing in Natural Sciences, Biomedicine and Engineering (COMCONF), International Conference on

作者： Veska Gancheva Kamelia Raynova Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

Medical image quality is very important. High quality ensures the standard of medical diagnosis, treatment, and patient life through health care or automated intelligence systems for medical diagnosis, monitoring, and treatment. The computing difficulties in processing medical images are discussed in the study. Proposing parallel computational models and program implementations based on medical image filtering techniques is one of the main issues. A filter-based parallel computational model is designed. Implementing a multithreaded parallel program verifies the suggested parallel model. An analysis of the effectiveness of medical image filters using a parallel multithreaded computer implementation that generates output images for each type of applied filter and applies filters on a list of compressed medical images. The BlackAndWhiteFilter, UVFilter, BinaryThresholdFilter, and RobertFilter have been applied. Experimental estimates have been analysed for the parallel performance metrics execution time and speedup. The performance estimation and scalability analyses demonstrate the strong scalability of the proposed solution.

关键词： Filters Computational modeling Scalability Instruction sets Computed tomography Image filtering Medical diagnosis Medical diagnostic imaging X-ray imaging Standards

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Neuromorphic Neurons and Networks for Artificial Intelligence Built Using Temporal Space Calculations 32

Neuromorphic Neurons and Networks for Artificial Intelligenc...

引用

32nd International Scientific Conference Electronics, ET 2023

作者： Trendafilov, Ianislav Gancheva, Veska Neuromorphica Ltd. 8 Kliment Ohridski blvd. Sofia1335 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350302004

We deigned a spiking neural network that computes network weights in the temporal dimension. Such a network can be used for artificial intelligence and deep learning. We demonstrate circuits implementing blocks for building such a network and then a training model. This enables creation of efficient Hessenstein-Reichardt detectors observed in motion detection networks in the nature. The proposed network allows reconfiguration across network layers algorithmically. © 2023 IEEE.

关键词： Neural networks

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An Approach to Investigating Plagiarism in Artificial Intelligence Content

An Approach to Investigating Plagiarism in Artificial Intell...

引用

computer Science (COMSCI), International Scientific Conference on

作者： Daniela Veleva Minkovska Elena Ventseslavova Antonova Kiril Andreev Koparanov Plamen Ognyanov Nakov Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350392166

ISBN: (纸本)9798350392173

A growing number of human-centric tasks like learning, planning, and creative writing require the integration of Artificial Intelligence (AI) in the current era of exponential advancements. Such systems collect and analyze data on their own making the best use of available resources and providing creative solutions for challenging issues. The impact of AI on productivity, data analysis, flexibility, and real-time decision- making in a variety of fields is examined in this paper. Focusing on academic settings, the paper considers ethical concerns surrounding AI-generated content. According to a survey, even though many students use ChatGPT for writing and research, worries about academic integrity and plagiarism still exist.

关键词： Surveys Productivity Ethics Plagiarism Writing Chatbots Real-time systems Trajectory Artificial intelligence Standards

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Input Data Time Range Impact on Forecast Quality Using Automated Procedures 11

Input Data Time Range Impact on Forecast Quality Using Autom...

引用

11th International Scientific Conference on computer Science, COMSCI 2023

作者： Koparanov, Kiril Andreev Antonova, Elena Ventseslavova Nakov, Ognyan Nakov Minkovska, Daniela Veleva Georgiev, Krasin Krasimirov Stoyanova, Lyudmila Yordanova Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd Sofia1000 Bulgaria Technical University of Sofia Faculty of Transport Department of Air Transport 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

Easy access to time series forecasting provides a strong competitive advantage for any company or organization. As automated forecasting environments become more prevalent in the market, it is crucial to explore their capabilities and features to utilize them competently. This study demonstrates the influence of data coverage on prediction accuracy and validates the initial assumption. Based on the findings, recommendations have been presented to enhance the quality of predictive processes. © 2023 IEEE.

关键词： Forecasting

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