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Quantum Scars in Microwave Dielec trie Photonic Graphene Billiards

Chinese Physics Letters 2020年第1期37卷 10-13页

作者： Xiao Wang Guo-Dong Wei Department of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024 School of Applied Science Taiyuan University of Science and TechnologyTaiyuan 030024

Brillouin *** closed graphene system has proven to be the ideal model to investigate relativistic quantum chaos *** electromagnetic material photonic graphene(PG)and electronic graphene not only have the same structural symmetry,but also have the similar band ***,we consider a stadium shaped resonant cavity filled with PG to demonstrate the relativistic quantum chaos phenomenon by numerical *** is interesting that the relativistic quantum scars not only are identified in the PG cavities,but also appear and disappear *** wave vector difference between repetitive scars on the same orbit is analyzed and confirmed to follow the quantization *** exploration will not only demonstrate a visual simulation of relativistic quantum scars but also propose a physical system for observing valley-dependent relativistic quantum scars,which is helpful for further understanding of quantum chaos.

关键词： structure. relativistic quantum

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

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8th International Conference on computer technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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Weather Prediction from Imbalanced Data Stream using 1D-Convolutional Neural Network 10

Weather Prediction from Imbalanced Data Stream using 1D-Conv...

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10th International Conference on Emerging Trends in Engineering and technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Alex, Suja A. Ghosh, Uttam Mohammad, Nazeeruddin St. Xavier's Catholic College of Engineering Department of Information Technology India Meharry School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States College of Computer Science Engineering Prince Mohammad Bin Fahd University Saudi Arabia

ISBN: (纸本)9781665467414

Data stream classification is a complex task in the real world due to its varying characteristics. The most common challenges are concept drift and class imbalance. Concept drift shifts in the underlying function generating the data. The biggest obstacle in achieving an effective classifier is due to class imbalance. In general, batch and ensemble solutions are mainly used to train the classifier with chunks of imbalanced data streams. In the paper, we proposed a framework called Self Organizing Auto-Encoder based 1D-CNN for Weather data forecasting. The objective of this framework is to classify imbalanced non-stationary data streams. A proposed model's concept drift handling strength is assessed using Population Stability Index (PSI). The Weather dataset is used for the evaluation of the proposed model. The results shows improvement in prediction accuracy and the classifier is stable with good PSI. © 2022 IEEE.

关键词： Neural networks Stability criteria Sociology Weather forecasting Information processing Market research Indexes

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Thunderstorm Nowcasting with Recurrent-Convolution Deep Learning: A Case Study in Ho Chi Minh City 18

Thunderstorm Nowcasting with Recurrent-Convolution Deep Lear...

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18th International Conference on Advanced Computing and Analytics, ACOMPA 2024

作者： Khang, Ta Gia Van Gia Thinh, Nguyen Van Hoai, Tran An, Phan Thanh Huu, Pham Thanh An, Nguyen Minh Bao Ho Chi Minh City University of Technology IVS Joint Stock Company Falculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Institute of Mathematical and Computational Sciences Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology IVS Joint Stock Company Falculty of Applied Science Ho Chi Minh City ADAI LAB Nha Trang City Viet Nam

ISBN: (纸本)9798331542467

Machine learning for precipitation nowcasting using radar reflectivity images is a promising approach in weather forecasting. This paper proposes a new pipeline of using Convolutional Neural Networks (CNNs) integrated with Gated Recurrent Units (LSTM) and visual Attention mechanisms for quantitative precipitation nowcasting (QPN) using radar reflectivity images. We propose an end-to-end nowcasting network that leverages a Convolutional-LSTM with Axial Attention as the backbone to develop an adaptive forecasting system. Our results demonstrate the potential of deep learning techniques to significantly improve short-term weather predictions. The models were trained and validated using real-world Doppler radar data from Nha Be, Ho Chi Minh City, ensuring both practical relevance and applicability. Experimental results highlight that the combination of 3D Convolution, Convolutional-LSTM, and Axial Attention achieves superior performance, underscoring the effectiveness of this approach. © 2024 IEEE.

关键词： Weather forecasting

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Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

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computers, Materials & Continua 2022年第4期71卷 109-124页

作者： Sathish Babu Pandu A.Sagai Francis Britto Pudi Sekhar P.Vijayarajan Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Mechanical Engineering Rohini College of Engineering&TechnologyPalkulam629401India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyAndra Pradesh530046India Department of Electrical and Electronics Engineering University College of EngineeringBIT CampusTiruchirappalli620024India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the *** Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation *** models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of *** this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and ***,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting *** order to perform long-term collection of weather data,Hadoop MapReduce tool is *** proposed solar irradiance prediction model operates on different ***,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data ***,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive *** is divided into input layer,hidden layer,loadbearing layer,and output *** overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is *** order to validate the performance of the proposed model,a series of experiments was *** experimental values infer that the proposed model outperformed other methods used for comparison.

关键词： Solar irradiation prediction weather forecast artificial intelligence Elman neural network mayfly optimization

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Machine Learning Algorithms for Predicting and Analyzing Arabic Sentiment

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SN computer science 2024年第8期5卷 1-10页

作者： Aladeemy, Amani A. Aldhyani, Theyazn H.H. Alzahrani, Ali Alzahrani, Eidah M. Khalaf, Osamah Ibrahim Alsubari, Saleh Nagi Deshmukh, Sachin N. Al-Adhaileh, Mosleh Hmoud Department of Computer Science & Information Technology Dr. Babasaheb Ambedkar Marathwada University MS Aurangabad India Applied College in Abqaiq King Faisal University Al-Ahsa 31982 Saudi Arabia Department of Computer Engineering King Faisal University Al-Alahsa 31982 Saudi Arabia Computer Science Department Al-Baha University Al Baha Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Deanship of E-Learning and Distance Education King Faisal University P.O. Box 4000 Al-Ahsa 31982 Saudi Arabia

Sentiment analysis (SA) is a specialized application within the domain of natural language processing (NLP) used to analyze textual input to identify underlying sentiments, emotions, and attitudes. Recent advancements in machine learning (ML) have demonstrated potential in accurately detecting sentiment in Arabic sentiment analysis (ASA). This paper investigates the efficacy of various feature extraction and ML methods for ASA, emphasizing unique Arabic data preprocessing techniques: removing stopwords, repetitions, punctuation, non-Arabic characters, hashtags, mentions, and URLs;deleting diacritics and tashkeel;stemming;tokenizing;and replacing emojis with text representations. We utilized two benchmark datasets, ASTC and Ar-Twitter, focused on social media content, to evaluate our methodology. The comparison involved two feature extraction approaches and five ML techniques: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and XGBoost. Our experimental investigation assessed datasets from Twitter on various topics, such as politics and arts, to evaluate their suitability for ASA. Feature extraction techniques included Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) with unigrams and bigrams. The aim of this comparison is also to explore the influence of feature extraction techniques in developing ASA systems. The best results were achieved using the RF model with unigram features, yielding an accuracy of 87.7% on the ASTC dataset, while the SVM model attained 90.3% accuracy on the Ar-Twitter dataset. This study highlights the importance of detailed preprocessing and robust feature extraction in improving ASA performance, setting a new benchmark for future research in Arabic NLP. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Arabic tweet dataset Deep learning Machine learning techniques Natural language processing Sentiment classification

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A Study on Prediction of Myocardial Infarction Using Computational Intelligence and Machine Learning Algorithms 2

A Study on Prediction of Myocardial Infarction Using Computa...

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2nd International Conference on Trends in Electrical, Electronics and computer Engineering, TEECCON 2023

作者： Subha, R. Nayana, B.R. Selvadass, M. M S Ramaiah Institute of Technology Department of EEE Bangalore India M S Ramaiah University of Applied Sciences Department of Computer Science Bangalore India Garden City University School of Professional Studies Bangalore India

ISBN: (纸本)9798350339949

One of the prominent causes of mortality and disability globally is myocardial infarction (MI). Early and accurate detection of MI is crucial to reduce the risks associated with it. Machine Learning (ML) approaches coupled with Computationally Intelligent Algorithms (CIA) have proved successful in many domains. In this work, an attempt has been made to predict and classify MI using ML techniques and advanced CIA for feature selection. Dataset comprising of patient records from Krasnoyarsk Inter district Clinical Hospital, Russia has been used to classify 12 different MI complications. To choose standout attributes for classification, four swarm intelligence-based algorithms have been used: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Artificial Bee Algorithm (ABA), and Bat algorithm (BA). For classification, three distinct classifier models-Random Forest (RF), Decision Tree (DT), and K Nearest Neighbor (KNN)-have been developed. In order to better understand the condition and contribute to the creation of novel diagnostic and preventative strategies, the study intends to discover and assess the efficiency of various CIA and ML algorithms in correctly predicting MI. © 2023 IEEE.

关键词： Machine learning

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Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks

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Nano 2025年第3期20卷

作者： Asif Raja, Muhammad Junaid Ali Masood, Zaheer I., Hussain A., Zameer A., Mehmood M.A.Z., Raja Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Electrical Engineering Capital University of Science and Technology Islamabad Pakistan Department of Computer and Information Sciences Pakistan Institute of Engineering & Applied Sciences Islamabad Pakistan School of Electrical and Data Engineering University of Technology Sydney Australia Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

In the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors τ1 and τ2 are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM's predictions and those from the numerical outcomes with MSE in the range of 10-7, underscoring the network's effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities. © 2025 World Scientific Publishing Company.

关键词： Firmware

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Efficiency analysis of deep learning-based object detection for safe human robot collaboration

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IFAC-PapersOnLine 2024年第19期58卷 1024-1029页

作者： Adam Dudek Justyna Patalas-Maliszewska Krzysztof Rokosz Faculty of Technical Science University of Applied Sciences in Nysa 48-300 Nysa Poland University of Zielona Gora Institute of Mechanical Engineering Poland Koszalin University of Technology Faculty of Electronics and Computer Science

Nowadays, deep-learning object-based detection is very often used for predicting and ensuring safety, in workspaces shared by human operators and robots. The main challenge is to evaluate the accuracy of such models depending on the acquired data, selected Artificial Intelligence architecture and on the relevant parameters for achieving the best efficiency. In this paper, the efficiency of detecting deep learning-based objects, applying the Region-Based CNN (YOLOv8 Tiny) was analysed for detecting objects within human robot collaboration (HRC).The relevant parameters were selected in each area discussed and the efficiency of the YOLOv8 Tiny applied for the object’s recognition was analysed. The research results indicate that the techniques for detecting objects, in HRC, applying YOLOv8 Tiny for interferences in the recorded material analysed the most was achieved by some 90%, however, in the context of the analysis of smoke disruption this was insufficient.

关键词： deep-learning object detection safety human robot collaboration (HRC) real data the efficiency a YOLOv8 Tiny

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Experimental Evaluation of Internet of Things Assisted Industrial Production Monitoring System using Smart Sensors 9

Experimental Evaluation of Internet of Things Assisted Indus...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Thangam, R. Sureka, V. Deepa, A. Vennila, C. Rajalingam, A. Haribabu, K. Srm University Department of Ece Tamil Nadu Chennai India S.A. Engineering College Computer Science and Engineering Tamil Nadu Chennai India Jeppiaar Institut of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Tamil Nadu Chennai India University of Technology and Applied Sciences College of Engineering and Technology Engineering Department Shinas Oman Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350384208

The main objective is to use the Internet of Things to build a system for controlling and monitoring equipment in factories, in which it is helpful to improve productions. When it comes to protecting your house, factory, workplace, or public space, monitoring systems are crucial. The integrated framework, Microcontroller, and connected sensors form the backbone of this protection setup. Smart sensors can identify when a person is moving across a room. At this point, the system sends out a signal indicating how many people have broken in within a given time frame and sounds an alert. The person identification Sensor is triggered into action once the protection system is switched on. This highly reactive method requires little processing power. This makes it an excellent component of an industrial monitoring system. This monitoring and protection system was built with a microcontroller and other sensors. These days, more and more business owners are investing in security systems as a means of keeping their assets safe and increasing the value of their establishments. In this effort, data transfer is accomplished through the Global System for Mobile communication (GSM) protocol. When data is collected, authorized workers enter it into a computerized spreadsheet at the branch office. Management merely needs to take action based on the results, which will be permanently recorded in the system. Automatic data gathering is an economical alternative to manual gathering of information for this kind of task. The useful reports for management are much improved as a result. Using this method also shortens the time required to pinpoint the problem. © 2023 IEEE.

关键词： Internet of things

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