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Advances in Data-Driven Analytics And Intelligent systems (ADACIS), IEEE International Conference on

作者： Zakariyaa Ait El Mouden Alae El Alami Mohammed Lahmer Abdeslam Jakimi Information and Communication Systems Engineering High School of Technology Moulay Ismail University Meknes Morocco Software Engineering Information Systems Engineering Faculty of Sciences and Techniques Moulay Ismail University Meknes Morocco

Graph clustering is a powerful technique used to identify and group similar nodes within a complex network structure. This procedure involves segmenting the graph into distinct groups, with the nodes in each group having strong interconnections or similar characteristics. In this survey, we will highlight a variety of existing approaches of graph clustering, including spectral clustering, modularity optimization and hierarchical clustering, to efficiently discover meaningful clusters, facilitating analysis and decision-making in diverse fields ranging from data science and network analysis to social sciences and bioinformatics.

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Systematic analysis of the role of different foods on breast, lung, and prostate cancer incidence

Food Chemistry Advances

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Food Chemistry Advances 2024年 4卷

作者： Prasath, S.Thanga Navaneethan, C. Research Scholar Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Medical professionals have long believed that our diet and health are intimately connected. Therefore, in this study, we thought of starting with the impact of cancer on the food we consume every day. Cancer stands as a leading cause of global mortality. It is assumed that environmental factors, including diet habits, primarily affect cancer risk. We often include meat in our regular diet because it is high in protein. In this systematic analysis, we examined the side effects of the meat that everyone loves to eat (red and processed). In line with the objective, we investigated how different types of meat and their subtypes consumption could accelerate the growth of breast cancer (BrC), lung cancer (LuC), and prostate cancer (PrC). Red and processed meat consumption was associated with an increased risk of overall cancer mortality, including breast, lung, and prostate cancer. Dose-response analyses revealed that 100 g/day increments of red meat and 50 g/day increments of processed meat consumption were associated with increased BrC, LuC, and PrC likelihoods. A notable phenomenon in this analysis was that the traditional foods of the respective countries protect their people from all types of cancer. © 2024

关键词： Breast cancer Lung cancer Processed meat Prostate cancer Red meat

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An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

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Computers, Materials & Continua 2022年第5期71卷 2721-2738页

作者： Aitizaz Ali Muhammad Fermi Pasha Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud Department of Software Systems and Cyber-Security School of ITMonash University47500Subang JayaMalaysia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499Korea Department of Information Technology College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif University11099Taif21944Saudi Arabia

Personal health records and electronic health records are considered as the most sensitive information in the healthcare *** solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent ***,we propose a novel framework and implemented an attribute-based access control system using ***,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security *** current methods only focus on the related clinical records received from a medical ***,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare *** have used a neural network and classifier to categorize the user access to our proposed *** proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare *** results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

关键词： Personal health records blockchain technology artificial intelligence access control attributes health care system

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An Evolutionary Algorithm-Based Neural Architecture Search for Feature Separation in Classification Problems

An Evolutionary Algorithm-Based Neural Architecture Search f...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Zhang, Qing Luo, YingMing Gong, Guoliang Lin, Lin School of Software Dalian University of Technology Dalian116620 China Alibaba Limited Hangzhou310099 China DUT-RU International School of Information Scienceand Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network and ServicSoftware of Liaoning Province Dalian116620 China Fuzzy Logic Systems Institute Iizuka820-0067 Japan

ISBN: (纸本)9798350318012

In this paper, we propose Evolutionary Algorithm-based Neural Architecture Search(EvoNAS), which use Evolutionary Algorithm to find the optimized Convolutional Neural Network(CNN) and combine with Distribution Estimation to improve efficiency during the search process to improve the classification performance. The optimized CNN reveal the process of classification. It can classify the features according to specified classes, improving the classification accuracy of similar features while achieving the desired classification accuracy. We conduct feature separation experiments On the publicly available CIFAR dataset, we use EvoNAS to conducte feature separation experimentson. EvoNAS is subjected to feature separation experiments on the publicly available CIFAR dataset. The classification accuracy has been improved by 0.55% and 0.56% respectively compare to the original network. In addition, the feature visualization and attribution visualization improve the classificatory interpretability of CNN. © 2023 IEEE.

关键词： Evolutionary algorithms

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Analysis of Legged Wheels with Spring Suspension for Stair-Climbing Robotics

Analysis of Legged Wheels with Spring Suspension for Stair-C...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Magomedov, Islam Aygumov, Timur Ordonez-Avila, Jose Luis Kadyrov Chechen state university Grozny Russia Dagestan State Technical University Department at of Software Engineering Computer Technology and Automated Systems Dagestan Russia San Pedro Sula Honduras

ISBN: (纸本)9798331511241

The following article looked into legged wheels with spring suspension. The idea behind the work was to see how adding springs in various places and their stiffness would impact the robot's ability to navigate through stairs. Due to the unconventional appearance of legged wheels, they are not suitable for even terrain passage. Hence, the idea was to add springs to minimize shock occurrence each time a leg touches the even environment. It was also important to equip springs throughout the whole path. As the robot progresses through steps, a greater disturbance of the structure occurs. Two different ideas were tested. In the first scenario, springs were attached between the holder of the wheels and the main body. In the second scenario, springs were directly added to the legs. The extruded part of the leg was divided into two, and a spring was embedded between these two parts. Different values of spring stiffness were also examined. The results illustrated that the structure would go further by implementing springs in all four wheels or in the rear. However, the structure was tested only on an upward incline. With other setups, it might be different with the rearrangement of the springs. It was also noted that with higher stiffness of the springs, the structure became unstable. In the second scenario, results illustrated the slightest progress. For instance, when the front wheels' legs were modified, they showed better results. To understand the influence in more depth, further investigation of the problem must be conducted. © 2025 IEEE.

关键词： Industrial robots

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Detection of Android Malware using Machine Learning

Detection of Android Malware using Machine Learning

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Fiky, Ahmed Hashem El Elshenawy, Ayman Madkour, Mohamed Ashraf Al-Azhar University Systems and Computer Engineering Faculty of Engineering Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

ISBN: (纸本)9781665412438

Nowadays, smartphones became an integral part of human life due to the great necessity for their daily activities. Most smartphone users are downloading and installing mobile apps without worrying about security. Therefore, smartphones are a winning goal for malware, mainly with Android devices. So, it was necessary to provide an intelligent model for detecting malware applications before installing them on android smartphones. In this paper, an intelligent model using machine learning algorithms is proposed for detecting the malware applications in smartphones based on the static malware analysis technique. The contents of an android application features are analyzed, such as permissions, intents, system commands, and API-calls that were extracted from the application manifest file and source code. The proposed model is applied on two separate datasets DREBIN and MALGENOME. The experimental results are compared with some other similar researches, and their results were analyzed. The results indicate that the performance of the proposed model outperforms other similar models for some selected classifiers and can reliably detect both malware and benign Android applications with high accuracy. © 2021 IEEE.

关键词： Android malware

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Robust Model Integral Predictive Control Design for Reference Tracking DC Servomechanism 10

Robust Model Integral Predictive Control Design for Referenc...

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10th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2021

作者： Khalil, Khalil Mohamed Elshenawy, Ayman Al-Azhar University Faculty of Engineering Systems and Computers Engineering Department Cairo Egypt Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo Egypt

ISBN: (纸本)9781665440769

Model Predictive Control (MPC) has been very successful in the control industries due to its ability to handle uncertainty in physical systems with some constraints. Due to its higher power density, DC servomechanism is used in control systems of several industrial applications. However, the DC servomechanisms suffer severe throttling loss, leading to low effectiveness, and the uncertainty parameters and distortions caused by the changing system environment harm their performance. In this paper, a Robust Integral MPC (RIMPC) model is designed to enhance the efficiency of the DC servomechanism control system. Uncertainties and nonlinearity of the parameters are taken into consideration. In RIMPC, MPC is incorporated into the integral controller synthesis through an optimization problem subjected to some Linear Matrix Inequality (LMI) constraints to generate the control law of the servomechanism dynamics. The performance of the proposed system is evaluated in two cases: with no disturbances and when disturbances are affecting the system. The obtained results are analyzed and compared with other controllers' design, such as Proportional Integral Derivative (PID) controller and the Monte Carlo (MC) controller. The obtained results prove that the proposed system is more effective in cost function and transient performance (rise time, peak time, maximum overshoot, and settling-time), indicating a much better reference tracking accuracy and proposed method robustness. © 2021 IEEE.

关键词： Model predictive control

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm

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Procedia Computer Science 2024年 235卷 3397-3407页

作者： B. Valarmathi N. Srinivasa Gupta V. Karthick T. Chellatamilan K. Santhi Dhanush Chalicheemala Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Assistant Systems Engineer Tata Consultancy Services Chennai Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today’s digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved.

关键词： COVID-19 Natural Language Processing Text Mining Long Short-Term Memory WordCloud Opinion mining Sentiment analysis Opinion extraction

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Accelerating Multi-agent Path Finding Based on Roadmap Construction in Non-grid Environment

Accelerating Multi-agent Path Finding Based on Roadmap Const...

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Asian Control Conference

作者： Zhenyu Yao Han Shen Yan Xu Yu Chen Jing Nie School of Cyber Science and Engineering Southeast University Nanjing China Department of Systems Science School of Mathematics Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Conflict-based search (CBS) is one of the widely used algorithms to solve multi-agent path finding (MAPF) problems. Though it combines optimality and completeness as a search algorithm, the time it takes to search for a solution grows exponentially as the number of agents gradually increases, and thus does not scale well. In this paper, a learning-based graph transformer is utilized as the heuristic function to accelerate planning in non-gird environment with roadmap generation. To solve unconnected graph problems when performing low-level path planning search in the CBS framework, triangulation method is introduced to obtain an optimized path with less nodes and edges. Simulation shows that the learning-based search method trained on roadmaps generated by constrained delaunay triangulation (CDT) leads to better solutions and a much greater success rate.

关键词： Search problems Transformers Real-time systems Path planning Planning Optimization

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Vehicle type recognition: a case study of MobileNetV2 for an image classification task 28th

Vehicle type recognition: a case study of MobileNetV2 for an...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Kobiela, Dariusz Groth, Jan Hajdasz, Michal Erezman, Mateusz Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Algorithms and Systems Modelling Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gabriela Narutowicza 11/12 Gdansk80-233 Poland

The goal of the research was to demonstrate the full data science lifecycle through a use case of the MobileNetv2 model for vehicle image classification task using various validation and test sets, each with different difficulty level. Diverse model variations were employed, each designed to recognize images of ground vehicles and classify them into one of five possible classes: car, truck, motorcycle, bicycle, or bus. In terms of validation accuracy, the highest results were obtained by the model trained with uniformly designed train and val sets (with data normalization and augmentation), where train set also contained validation set. This model also obtained the highest accuracy results on both test sets. The superiority of MODEL 3 BASELINE is confirmed by other metrics as well: test loss, f1-score, AUC and confusion matrices (for both test sets). Results between MODEL 1 BASELINE and MODEL 2 BASELINE differed according to the test set 1 and 2 and other metrics and it was not possible to declare the superiority of one method of datasets preparation over another (original class distribution [no data normalization and no data augmentation] versus uniformly designed [with data normalization and augmentation]). The article also presents challenges and findings - the problems, key issues, and their solutions that arose during the process of data collection and tagging, as well as the preparation and evaluation of the model. © 2024 The Authors.

关键词： Classification (of information)

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