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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Delimayanti, Mera Kartika Mardiyono, Anggi Warsuta, Bambang Puspitaningrum, Eka Suci Naryanto, Rizqi Fitri Naryaningsih, Agustien Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia Fisheries Research Center Indonesia

ISBN: (纸本)9798350320954

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard. © 2023 IEEE.

关键词： COVID-19

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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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Characterizing land deformations in Western Greece with satellite remote sensing observations and in situ ground monitoring 14

Characterizing land deformations in Western Greece with sate...

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Earth Resources and Environmental Remote Sensing/GIS Applications XIV 2023

作者： Fasoulis, Konstantinos Triantafyllidis, Panagiotis Hadjidoukas, Panagiotis Pappas, Christoforos Department of Civil Engineering University of Patras Patras265 04 Greece Department of Computer Engineering and Informatics University of Patras Patras265 04 Greece

ISBN: (纸本)9781510666979

The area of Western Greece and the Peloponnese region show land deformations that can be triggered, for example, by seismic activity (e.g., Corinthian Gulf faults) and environmental factors (e.g., heavy rainfall). Land subsidence observed during the recent years within the Campus of the University of Patras in Northwestern Peloponnese confirms these spatiotemporal dynamics, with pronounced effects on the existing infrastructure and facilities. Here, we combined satellite remote sensing techniques and in situ geodetic monitoring to better understand these patterns and possible driving mechanisms. Regional-scale land deformations were quantified with active microwave remote sensing techniques (Persistent Scatterer Interferometric Synthetic Aperture Radar, PSInSAR) based on SAR data from the European Space Agency Sentinel-1 constellation of satellites. Land cover dynamics were characterized with high-resolution optical remote sensing observations (Normalized Difference Vegetation Index, NDVI;Planet Labs). The spatiotemporal Sentinel-1 SAR data were processed using the SNAP-StaMPS (SentiNel Application Platform-Stanford Method of Persistent Scatterer) integrated workflow for Persistent Scatterers Interferometry. The PSInSAR analysis resulted in regional-scale displacements of up to a few tens of mm per year. These displacements patterns were explored in tandem with possible land cover changes as detected with the NDVI spatiotemporal dynamics and further refined at the local scale with repeated field surveys during 2022 and 2023 at selected locations within the Campus of the University of Patras that were affected by land subsidence. The combination of remote sensing techniques, together with in situ ground monitoring, provides unique opportunities for monitoring ground deformation in almost real-Time, facilitating thus the assessment of the response of infrastructures to such spatiotemporal changes. © 2023 SPIE. All rights reserved.

关键词： Interferometry

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Enhancing Energy Efficiency and Sustainability with Hybrid AC/DC Microgrids for Energy Transition 3

Enhancing Energy Efficiency and Sustainability with Hybrid A...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus PV-Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350375923

The transition to a lower-carbon energy system across Europe brings numerous opportunities and challenges, necessitating advancements in sustainable and eco-friendly energy solutions. This study examines the design and operation of distribution networks and microgrids, particularly emphasizing the integration of hybrid AC/DC distribution systems. The primary goals of this integration are to enhance overall system performance and reduce operating expenses. Utilizing DIgSILENT Power Factory software, the research simulates a microgrid at the FOSS Research Center for Sustainable Energy at the University of Cyprus, incorporating real load and PV generation data. In order to reduce the amount of energy imported from the main grid, this approach evaluates various combinations of AC and DC loads, power production units, and energy storage solutions. The results show that the most optimal structure, with 65% DC loads and 35% AC loads, maximizes efficiency as well as minimizes running expenses. The analysis highlights a notable decrease in imported energy, which dropped from 60.3 kWh/day at full AC loads to 51.6 kWh/day at the optimal DC ratio. Finally, the design and implementation of effective, sustainable energy systems that are in line with changing European energy policy and technology breakthroughs will be significantly impacted by these outcomes. © 2024 IEEE.

关键词： Energy transition

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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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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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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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Reliable Service Recommendation: A Multi-modal Adversarial Method for Personalized Recommendation under Uncertain Missing Modalities

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IEEE Transactions on Services Computing 2025年第3期18卷 1724-1738页

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering China Huazhong Agricultural University College of Informatics Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong DSA Thrust and IoT Thrust Center China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications

关键词： Discriminators

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Fundamental Dynamics of Popularity-Similarity Trajectories in Real Networks

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Physical Review Letters 2024年第25期132卷 257401-257401页

作者： Evangelos S. Papaefthymiou Costas Iordanou Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus.

Real networks are complex dynamical systems, evolving over time with the addition and deletion of nodes and links. Currently, there exists no principled mathematical theory for their dynamics—a grand-challenge open problem. Here, we show that the popularity and similarity trajectories of nodes in hyperbolic embeddings of different real networks manifest universal self-similar properties with typical Hurst exponents H≪0.5. This means that the trajectories are predictable, displaying antipersistent or “mean-reverting” behavior, and they can be adequately captured by a fractional Brownian motion process. The observed behavior can be qualitatively reproduced in synthetic networks that possess a latent geometric space, but not in networks that lack such space, suggesting that the observed subdiffusive dynamics are inherently linked to the hidden geometry of real networks. These results set the foundations for rigorous mathematical machinery for describing and predicting real network dynamics.

关键词： Evolving networks Real world networks Evolving network models Self-similarity

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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