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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Zavalsiz, Muhammed Talha Alhajj, Sleiman Sailunaz, Kashfia Ozyer, Tansel Alhajj, Reda Istanbul Medipol University Department of Computer Engineering Istanbul Turkey International School of Medicine Istanbul Medipol University Istanbul Turkey University of Calgary Department of Computer Science Alberta Canada Ankara Medipol University Department of Computer Engineering Ankara Turkey University of Southern Denmark Department of Health Informatics Odense Denmark

ISBN: (纸本)9798350320244

Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging provides a way for the internal structure of the human body to be visible with various methods. With DL models, cancer detection, which is one of the most lethal diseases in the world, from medical images can be made possible with high accuracy. The main objective of this paper is to detect Pancreatic Cancer, which is one of the cancer types with the highest fatality rate, from a dataset of Computed Tomography (CT) images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. The designed DL model is integrated into the Flask application to develop a web application. With this application, early diagnosis of pancreatic cancer can be achieved, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started. Due to the abundance of medical images reviewed by medical professionals, this application can assist radiologists and other specialists in Pancreatic Tumor detection. © 2022 IEEE.

关键词： Convolutional neural networks

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Fine-Grained Cooperative Coevolution in a Single Population: Between Evolution and Swarm Intelligence 1

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15th Biennial International Conference on Artificial Evolution, EA 2022

作者： Lutton, É. Al-Maliki, S. Louchet, J. Tonda, A. Vidal, F.P. UMR 518 MIA INRAE Palaiseau France School of Computer Science and Electronic Engineering Bangor University Bangor United Kingdom Computer Science Department College of Science Basrh University Basra Iraq EFREI Villejuif France

ISBN: (数字)9783031426162

ISBN: (纸本)9783031426155

Particle Swarm Optimisation (PSO) and Evolutionary Algorithms (EAs) differ in various ways, in particular with respect to information sharing and diversity management, making their scopes of applications very diverse. Combining the advantages of both approaches is very attractive and has been successfully achieved through hybridisation. Another possible improvement, notably for addressing scalability issues, is cooperation. It has first been developed for co-evolution in EA techniques and it is now used in PSO. However, until now, attempts to make PSO cooperate have been based on multi-population schemes almost exclusively. The focus of this paper is set on single-population schemes, or fine-grained cooperation. By analogy with an evolutionary scheme that has long been proved effective, the fly algorithm (FA), we design and compare a cooperative PSO (coPSO), and a PSO-flavoured fly algorithm. Experiments run on a benchmark, the Lamp problem, show that fine-grained cooperation based on marginal fitness evaluations and steady-state schemes outperforms classical techniques when the dimension of the problem increases. These preliminary results highlight interesting future directions of research on fine-grained cooperation schemes, by combining features of PSO and FA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Swarm intelligence

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Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis

arXiv

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arXiv 2022年

作者： Ratul, Ishrak Jahan Wani, Ummay Habiba Nishat, Mirza Muntasir Al-Monsur, Abdullah Ar-Rafi, Abrar Mohammad Faisal, Fahim Kabir, Mohammad Ridwan Department of Electronic and Electronic Engineering Department of Computer Science and Engineering Gazipur Bangladesh

Bone Marrow Transplant (BMT), a gradational rescue for a wide range of neoplastic, chronic, and allied disorders emanating from the bone marrow, is an efficacious surgical treatment. Several risk factors, such as post-transplant illnesses, new malignancies, and even organ damage, can impair long-term survival after BMT. Therefore, technologies like Machine Learning (ML) are being linked to hematology for investigating the survival prediction of BMT receivers along with the influences that limit their resilience. In this study, a comprehensive approach is undertaken where an efficient survival classification model is presented, incorporating the Chi-squared feature selection method to address the dimensionality problem and Hyper Parameter Optimization (HPO) to increase accuracy. A synthetic dataset is generated by imputing the missing values, transforming the data using dummy variable encoding, and compressing the dataset from 59 features to the 11 most correlated features using Chi-squared feature selection. The dataset was split into two sections (train and test set) with a ratio of 80:20, and the hyper-parameters were optimized using Grid Search Cross-Validation (GSCV). Several supervised ML methods were trained in this regard, like Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gradient Boosting Classifier (GBC), Ada Boost (AdB), and XG Boost (XGB). The simulations have been performed for both the default and optimized hyperparameters by using the original and reduced synthetic dataset. After ranking the features using the Chi-squared test, it was observed that the top 11 features with HPO, resulted in the same accuracy of prediction (94.73%) as the entire dataset with default parameters. Moreover, this approach requires less time and resources for predicting the survivability of children undergoing BMT. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory acc

关键词： Decision trees

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BAT Optimized SMC for Continuous Stirred Tank Reactor

BAT Optimized SMC for Continuous Stirred Tank Reactor

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Husam E. Elfadel Haroon Congzhi Huang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electrical and Electronic Engineering Faculty of Engineering Omdurman Islamic University Khartoum Sudan

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This topic proposed a Sliding Mode Control (SMC) strategy utilizing the BAT Algorithm (BAT-SMC) for regulating concentration and temperature in a Continuous Stirred Tank Reactor (CSTR) system. The SMC is designed to maintain concentration (CA) and temperature (T) within desired setpoints by adapting sliding surfaces in the presence of disturbances and system uncertainties. The BAT Algorithm enhances the SMC by optimizing control parameters, reducing chattering effects, and improving tracking accuracy. Simulations validate the efficacy of BAT-SMC, showing improved dynamic performance for both concentration and temperature control compared to other controllers.

关键词： Uncertainty Mechatronics Heuristic algorithms Optimal control Coolants Temperature control Continuous-stirred tank reactor Feeds Information technology Sliding mode control

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Advanced Automated Vulnerability Scoring: Improving Performance with a Fine-Tuned BERT-CNN Model

Advanced Automated Vulnerability Scoring: Improving Performa...

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International Symposium on Telecommunications, IST

作者： Seyedeh Leili Mirtaheri Andrea Pugliese Narges Movahedkor AmirHossein Majd Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Rende Italy Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnique) Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

The ever-growing prevalence of software systems necessitates robust and efficient vulnerability assessment methods. An automated vulnerability assessment model can significantly streamline this process, providing accurate identification of vulnerability severity levels based on the given textual descriptions. This paper addresses this critical need by proposing a novel vulnerability score predictor model. Our model uses Bidirectional Encoder Representations from Transformers (BERT) and deep learning to classify vulnerability metrics accurately. We have fine-tuned the BERT tokenizer on NVD’s descriptions and used TextCNN for better performance. This approach represents a significant advancement in the field of automated vulnerability assessment, offering substantial benefits for security professionals by streamlining vulnerability identification and prioritization. The carried out experimental evaluations in comparison to various deep learning models, including LSTM, CNN, and LSTM-Attention, as well as two state-of-the-art automated vulnerability assessment baselines indicate an accuracy score of over $\mathbf{9 7 \%}$ across all vulnerability metrics.

关键词： Measurement Deep learning Accuracy Bidirectional control Predictive models Transformers Software systems Encoding Telecommunications Security

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Properties of Distributed Filtering Under Scaled Noise Covariances

Properties of Distributed Filtering Under Scaled Noise Covar...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Xiaoxu Lyu Guangzhi Tian Suyang Hu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper investigates the properties of the distributed filter under scaled noise covariances. Initially, three performance indices, along with three auxiliary indices, are introduced to evaluate the performance of the distributed filter. By providing the specific assumptions on the scaled parameters, the proportional relations among three performance indices are revealed. The relations of the gain matrices under the nominal and actual parameter scenarios are also examined. Furthermore, the impact of the fusion step on these relations is elucidated. These results provide guidance for designing the nominal noise covariance and evaluating the performance of the distributed filter under the scaled noise covariances. The theoretical results are validated through simulations.

关键词： Automation Filtering Noise Covariance matrices Robots

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Modeling the Health Status of a Ball Bearing for Predictive Maintenance Purposes

Modeling the Health Status of a Ball Bearing for Predictive ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Elisavet Karapalidou Agisilaos Efraimidis Stavros Vologiannidis Efstathios Antoniou Department of Computer Informatics and Telecommunications Engineering International Hellenic University Terma Magnisias Serres Greece Department of Informatics and Electronics Engineering International Hellenic University Thessaloniki Greece

Modern industries are constantly aiming to maximize utilization and performance of their machine equipment and minimize costly and unscheduled downtime. In the recent years, this was made possible with the advancement of technology and the coming of the fourth industrial revolution, also known as Industry 4.0, which introduced the Internet of Things and Artificial Intelligence systems in industrial applications. This allowed the employment of predictive maintenance methods able to assess the health of the equipment and diagnose possible future failures. In this work, an experimental assembly was constructed comprised by an electrical motor, a reduction gear, an axle, a coupler, a bearing and a vibration sensor attached to the latter. Three types of experiments were conducted for the purpose of producing a labeled dataset based on vibration measurements. Specifically, measurements were made during the bearing's working in normal operating state, one with lack and one with excess amount of grease. A preliminary statistical analysis was performed while a Multilayer Perceptron model was employed to automatically predict the status of the bearing. The dataset is freely available at the Zenodo data repository.

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Distributed Functional Compression for Independent Component Analysis in Wireless Networks

Distributed Functional Compression for Independent Component...

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IEEE Conference on Global Communications (GLOBECOM)

作者： A. Alamoudi Y. Saidutta F. Fekri Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah Saudi Arabia School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we consider distributed Independent Component Analysis (ICA) in wireless networks, where data from several geographically distributed wireless nodes (nodes) must be transmitted to a central server (server) to extract original sources through ICA. However, transmitting the vast amount of data over wireless channels to the server poses significant challenges due to limited bandwidth and privacy concerns. Our research addresses how to encode node data to meet channel rate constraints while providing maximally relevant information for ICA. Particularly, we propose a distributed functional compression framework for learning ICA over orthogonal AWGN channels. The framework leverages the Information Bottleneck (IB) principle to encode and compress the data to meet the channel rate constraint while maximally preserving the functionally relevant information for ICA. We train both neural encoders at the nodes and a neural decoder at the server in an unsupervised manner using the IB principle. We consider ICA for both linear and nonlinear mixing setups. Compared to the state-of-the-art, over real dataset, our proposed framework demonstrates a remarkable improvement of up to approximately 43% in accurately estimating the source signals in ICA while meeting the channels’ rate constraints. Finally, we propose a three-stage training algorithm, where the raw sensory data never leaves the nodes either for training or inference, to reduce the communication overhead. We show that our proposed training algorithm notably reduces channel use compared to the traditional cloud-based method, where the observed data from the nodes are compressed and transmitted to the cloud for learning ICA.

关键词： Training Wireless sensor networks Wireless networks Independent component analysis Approximation algorithms Inference algorithms Decoding Servers Uplink Unsupervised learning

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Development of Accurate Position Estimation Device Using Rtk Module Based on GNSS

Development of Accurate Position Estimation Device Using Rtk...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Chulsun Park Sangho Goh Hyuksoo Jang Sungkwon Park Department of Electronic and Computer Engineering Hanyang University Seoul South Korea Department of Electronic Convergence Engineering Hanyang University Seoul South Korea

A global navigation satellite system (GNSS) is a position estimation technique using satellites and is now widely used in various fields. However, there are many factors between the rover and the satellite that interfere with position estimation. Satellite time error, orbital error, ionospheric/tropospheric refraction, and multiple paths are some of the factors that prevent accurate position estimation. To improve the position estimation accuracy, real-time kinematic (RTK) has been developed. Therefore, in this paper, we develop a device with RTK technology to improve the position estimation accuracy of a rover. We developed a device with an accuracy of about 10 cm or less using RTK technology.

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U-shape-based architecture with Adjusted Bilateral Guided Aggregation Layer for nuclei image segmentation 9

U-shape-based architecture with Adjusted Bilateral Guided Ag...

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9th IEEE International Conference on Communications and electronics, ICCE 2022

作者： Nguyen, Viet-Dung Nham, Do-Hai-Ninh Pham, Van-Truong Tran, Thi-Thao Hanoi University of Science and Technology School of Applied Mathematics and Informatics Hanoi Viet Nam Hanoi University of Science and Technology School of Electrical and Electronic Engineering Department of Automation Engineering Hanoi Viet Nam

ISBN: (数字)9781665497459

ISBN: (纸本)9781665497442

Medical image segmentation with AI models has been showing its incredible improvements recently and one of the most significant building blocks is U-shape architecture. With that being said, a big issue of encoder-decoder based models is the lack of semantic information during the decoding process despite the backing up of the skip connection layer. In this paper, we address this challenge with our proposed model. Inheriting the idea of spatial path and semantic path from BiSeNetV2, we replaced the skip connection between encoder and decoder blocks with Adjusted Bilateral Guided Aggregation (ABGA) layer. In addition, we also leveraged the efficiency and elegance from EffecientNet as the model's encoder. Besides that, a new loss function based on Kulczycki 2 coefficient is introduced. © 2022 IEEE.

关键词： Semantics

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