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24th Conference Information Technologies - Applications and Theory, ITAT 2024

作者： Bobe, Radu Traian Gheorghe, Marian Ipate, Florentin Niculescu, Ionuţ Mihai Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Str Academiei 14 Bucharest010014 Romania Faculty of Engineering and Informatics University of Bradford Bradford West YorkshireBD7 1DP United Kingdom

Numerical P systems are compuational models which are inspired by cellular processes and use numerical values inside the membrane structure. In this paper we will present the variants of numerical P systems that have been proposed in the literature. In particular, we will discuss the advantages introduced by these extensions in terms of Turing completeness and potential applications. Moreover, using a reference example, we will experiment a mapping between some important numerical P systems variants. © 2024 CEUR-WS. All rights reserved.

关键词： Membrane structures

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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Overview of Load Disaggregation Models, Methods and Hardware 59

Overview of Load Disaggregation Models, Methods and Hardware

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59th International Universities Power engineering Conference, UPEC 2024

作者： Lancranjan, Mircea N. Muresan, Alexandru Jurj, Dacian I. Berciu, Alexandru Polycarpou, Alexis Czumbil, Levente Micu, Dan D. Technical University of Cluj-Napoca Department Electrical Engineering Cluj-Napoca Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (纸本)9798350379730

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated. © 2024 IEEE.

关键词： AI Data Cleaning Energy Efficiency Energy Signature Load Disaggregation Load Monitoring NILM

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A comparative analysis of the effectiveness of feature engineering techniques on thyroid disease prediction 1

A comparative analysis of the effectiveness of feature engin...

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Mashonga, Aidah Kudzainyandoro, Leslie Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

The thyroid gland's edge experiences an abnormal proliferation of thyroid tissue, which causes thyroid illness. The two primary types of thyroid disorders are hypothyroidism and hyperthyroidism which typically result when this gland releases excessive amounts of hormones. To identify and diagnose thyroid disease, this study suggests employing effective classifiers and feature selection strategies that consider accuracy and other performance evaluation measures. This study offers a thorough examination of various classifiers that includes the support vector machine, logistic regression, and extreme gradient boosting algorithms. The algorithms use three feature removal strategies that is recursive feature elimination, Pearson's correlation and chi-squared statistics. To determine thyroid illness, thyroid data from the Kaggle datasets were used. Numerous aspects of the experiment have been evaluated and analyzed, including accuracy, precision, and the receiver operating curve's area under the curve. The outcome showed that classifiers that use feature selection have a greater overall accuracy(Xtreme Gradient Boost 98%and support vector machine 95%) compared to without feature selection technique (support vector machine 89%). Logistics regression performed better without at 95% than 94% with feature selection. © 2022 IEEE.

关键词： Feature Selection

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An Evaluation of the Dual Vectors Based Solutions for Robot-World/Hand-Eye Calibration 1

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25th International Symposium on Measurement and Control in Robotics, ISMCR 2023

作者： Ganciu, Aura Burlacu, Adrian Department of Automatic Control and Applied Informatics Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iași Iași Romania

ISBN: (数字)9783031510854

ISBN: (纸本)9783031510847

The hand-eye calibration problem represents a major challenge in robotics, arising from the widespread usage of robotic systems along with robot-mounted sensors. Briefly, consisting of estimating the position and orientation of a visual sensor (usually a camera attached to the robot’s end-effector) with respect to the robot itself, calibration is critical in tracking rigid bodies—a common task within mixed reality applications. Hand-eye calibration implies solving the problem formulated as AX = XB or AX = YB equations. The solution lies in computing the unknown homogeneous transformation matrices X and Y, given the A and B parameterizations of rigid body motions. We present the dual vectors based approaches for AX = XB and AX = YB calibration problems, making use of the isomorphism between the special Euclidean group and the orthogonal dual tensor group. Comparisons with state-of-the-art algorithms are used to assess our obtained results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Mixed reality

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Test-Driven Simulation of Robots Controlled by Enzymatic Numerical P Systems Models 15th

Test-Driven Simulation of Robots Controlled by Enzymatic N...

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15th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2023

ISBN: (纸本)9783031575228

The simulation of robots behavior and the use of robust models are very important for building controllers. Testing is an important aspect in this process. In this paper, a test-driven approach for designing robot controllers based on enzymatic numerical P systems models is introduced. Four such models are defined and tested using three distinct scenarios. The paper reveals an effective way of using modelling, simulation and testing in a coherent way. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Controllers

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Fake News Detection using 5L-CNN 1

Fake News Detection using 5L-CNN

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Collen, Demo Rangarirai Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

With the advent of websites and social media technologies, the internet has been a great method of transmitting news and information around the globe. However, due to lack of editorial scrutiny and monitoring by the media authorities, the news distribution has been seriously abused leading to the fast spreading of fake news. Fake news involves misleading broadcasting of information from sources that target to intentionally manipulate the way how people view some events or statements. This study seeks to develop a fake news detection model which will be able to analyze the title and text information attached to the news articles. From the researches done by previous scholars, it showed that the models did not perform very well because of the lack of sufficient feature extraction and fine tuning of the classification of the text. This research will therefore, articulate the gap by employing 5L-CNN deep learning model which will use inbuilt tokenizers for word embedding and will show better accuracy compared to the traditional machine learning models. In this paper, we compare machine learning algorithms (Decision trees, Random Forest, Logistic Regression & Naive Bayes) and deep learning algorithms (RNN, 5L-CNN & LSTM) to classify the authenticity of news articles. The model developed in this paper attained the best accuracy with 5L-CNN which had a result of 99.99%. © 2022 IEEE.

关键词： Decision trees

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Implementation of a Mixed Triple Logistic Map and the BB84 Quantum Key Distribution for Secure Image Communication

Implementation of a Mixed Triple Logistic Map and the BB84 Q...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Morissa, Virnika Shefira Gina Setiadi, De Rosal Ignatius Moses Universitas Dian Nuswantoro Informatics Engineering Department Semarang50131 Indonesia Universitas Dian Nuswantoro Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Semarang50131 Indonesia

ISBN: (纸本)9798350366648

In the digital era, data communication security is one of the main challenges. This research proposes a new method for secure image communication, which is a mixed triple logistic map and BB84 quantum key distribution. This approach combines the strengths of classical and quantum cryptographic systems to improve security. BB84 quantum key distribution protocol for securely distributing encryption keys. The resulting key is then processed into a hash value using the SHA-256 algorithm. These hash values are converted into ASCII values, which are used to generate three key sequences using the modified logistic map. The three key sequences are combined to form a keystream used in image encryption. Several security metrics are used to evaluate encryption security, including entropy, the Chi-Square test, and pixel correlation. The analysis results show that the encrypted image has an entropy close to eight. In addition, all chi-square values smaller than 293 indicate a uniform distribution. The correlation of nearby pixels in the encrypted image is also very low, where all values are less than 0.01, which indicates the loss of local patterns in the encrypted image. These results demonstrate that the proposed method effectively secures image communications, providing high security and resistance to conventional and quantum cryptographic attacks. © 2024 IEEE.

关键词： Quantum cryptography

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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Quadratic Weighted Kappa Score Exploration in Diabetic Retinopathy Severity Classification Using EfficientNet 1

Quadratic Weighted Kappa Score Exploration in Diabetic Retin...

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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Chivinge, Lincoln Nyandoro, Leslie Kudzai Zvarevashe, Kudakwashe University of Zimbabwe Faculty of Computer Engineering Informatics and Communication Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

Diabetic Retinopathy (DR), chronic progressive disease of the eye, may give rise to permanent sight loss. Clinicians use fundus pictures to check if DR is present and rely on physicians to diagnose the stage or severity by visual inspection of the images. In relying on a clinician's subjective prognosis, this is deemed a procedure that takes a lot of time and susceptible to misjudgements. In discovering DR, poor Quadratic Weighted Kappa (QWK) scores have resulted from poor quality of pictures and imbalanced distribution of classes. Even though studies have shown high accuracy, sensitivity, specificity and ROC metrics, their limitation is that they do not consider the level of disparity across the classified labels. The QWK score demonstrates that even if an algorithm presents high accuracy, it is still not best fit to classify DR into its 5 classes. Many researchers have tried fine-tuning the neural network to create noise-resistant deep learning and recorded high accuracy and sensitivity but low QWK scores. The problem with the other methods is mainly pre-processing of the images and model building patterns. Most of the studied literature lacks the image augmentation step which might lead to an erroneous result. This research aims to create an algorithm from deep learning models with a data augmentation step and demonstrate how important it is for attaining better QWK scores for all stages of diabetic retinopathy. The model met study objectives and obtained an accuracy of 93% and a QWK score of 0.961. The outcomes show that the method can make accurate predictions without need for human feature extraction and that it may be used as an early DR diagnostic and staging screening tool. © 2022 IEEE.

关键词： Diagnosis

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