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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Pegah Ardehkhani Pouya Ardehkhani Haniyeh Hooshmand Department of Industrial Engineering Sharif University of Technology Tehran Iran Deep Learning Research Lab Department of Computer Engineering Faculty of Engineering University of Tehran Tehran Iran Department of Computer Engineering Faculty of Engineering University of Tehran Tehran Iran

This study focuses on the task of Persian numeral classification within image data, employing the Vision Transformer (ViT) architecture to predict numerals akin to the MNIST dataset, but adapted to the Persian script. Our approach yielded a notable validation accuracy of 0.9920, particularly noteworthy when employing a patch size of 4. Notably, the research introduces an innovative visualization aspect, showcasing the first multi-head attention linear map and its counterpart, the last one. The visualization of these attention maps provides a unique insight into the model’s internal processes and highlights its proficiency in capturing intricate patterns within Persian numeral images. This work contributes to the evolving landscape of character recognition, specifically addressing the challenges posed by the Persian script, and underscores the efficacy of employing the ViT architecture for such intricate tasks. The achieved validation accuracy and the detailed visualization of attention maps mark notable milestones in the realm of Persian numeral classification, showcasing the potential of Vision Transformers in the context of script-specific optical character recognition.

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Depression Detection on e-Risk 2017 using Long Short-term Memory Models

Depression Detection on e-Risk 2017 using Long Short-term Me...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Farnaz Sheikhi Laya Fakher Danial Chekani Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran

In today’s fast-paced world, individuals are increasingly vulnerable to high levels of stress, which consequently raise the risk of developing depression. Depres-sion may represent itself via various symptoms, ranging from emotional to physical pains. The symptoms appear gradually, deceiving patients to ignore their changes. The reluctance to accept the depression and the social stigma surrounding this mental disorder prevent a remarkable fraction of patients from vising the specialists and receiv- ing the treatments. Although people feel uncomfortable to reveal themselves in face-to-face connections, they have an opposite attitude towards social networks. People willingly share their opinions, thoughts, and feelings on social networks nowadays. Psychological studies have explored potential differences in the literature used by individuals with depression and those without. Hence, social network data has become a valuable source of information to study the mental health of users. Following this motivation, in this paper, we have examined the power of several deep learning algorithms for automati- cally detecting depression on eRisk 2017 dataset, obtained from the social network of Reddit. To this aim, we have studied and compared Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), and hybrid LSTM- GRU models for depression detection. The results have illustrated superiority of LSTMs in terms of accuracy and timing of detecting depression over the rest of the models. These findings contribute to the expanding body of research on using artificial intelligence for detecting mental health issues.

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Prediction of Heart Disease Diagnosis using Machine Learning Algorithms

Prediction of Heart Disease Diagnosis using Machine Learning...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Parul Balvir Khushi Katore Palash Gourshettiwar Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The prevalence of heart disease is rising globally, with the World Health Organization reporting that cardiovascular diseases account for approximately 17.9 million deaths annually. Early diagnosis and treatment are critical to mitigating risks, as studies indicate that timely intervention can reduce mortality rates by up to 30%. This study presents a predictive numerical model leveraging machine learning classifiers—Random Forest, K-Nearest Neighbours (KNN), and Logistic Regression—to enhance diagnostic accuracy. The dataset utilized is sourced from the University of California, Irvine (UCI), comprising crucial patient characteristics. A comparative analysis of classifiers is performed, evaluating their efficacy in heart disease prediction. The study provides insights into feature selection, classification performance, and the impact of different modeling approaches, contributing to improved diagnostic frameworks in medical applications. Additionally, a detailed comparison of existing methods and their limitations has been incorporated.

关键词： Heart Logistic regression Renewable energy sources Accuracy Organizations Nearest neighbor methods Predictive models Solids Random forests Diseases

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Novel Approach to Image Similarity Estimation and Object Matching: Leveraging ViT Architecture and Euclidean Distance Metric

Novel Approach to Image Similarity Estimation and Object Mat...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Pegah Ardehkhani Pouya Ardehkhani Haniyeh Hooshmand Department of Industrial Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Deep Learning Research Lab Faculty of Engineering University of Tehran Tehran Iran Department of Computer Engineering Faculty of Engineering University of Tehran Tehran Iran

In addressing the challenge of image similarity estimation on the MNIST dataset, our research drives from conventional Siamese network methodologies by incorporating Vision Transformer (ViT) architecture. Departing from the standard MNIST dataset, we introduced a novel paired dataset tailored to enhance the capabilities of similarity estimation. The innovation lies in the utilization of ViT as the core foundation for extracting features, followed by the application of Euclidean distance metrics on the dual input. This departure from the traditional approach not only broadens the scope of image similarity assessment but also enhances the model’s discriminative power. Notably, the model attains a commendable test accuracy of 97.14% with a patch size of 7, underscoring the efficacy of our proposed methodology. This work it not only adds to the changing scenery of the image similarity estimation while also emphasizing the importance of leveraging non-conventional architectures to achieve enhanced performance in this domain.

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge Systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 IEEE.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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Synergistic Swarm Optimization Algorithm

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computer Modeling in engineering & Sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

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Prototype Proposal of an 18 kW Non-Isolated Bidirectional Converter for Battery Energy Storage System

Prototype Proposal of an 18 kW Non-Isolated Bidirectional Co...

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Power Electronics (Ee)

作者： Andrej Brandis Kristian Knol Denis Pelin Danijel Topić Department of Electromechanical Engineering Faculty of Electricity Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

In this paper, a brief overview of converter topologies used in stationary battery energy storage systems is given. A simulation model of converter was developed in MATLAB Simulink. A simulation is conducted to provide a preliminary understanding of the converter's bidirectional nature. The results indicate that the state of charge (SOC) increases during battery charging and decreases during discharging. The topology of non-isolated two-stage bidirectional converter is presented for proposed real-world prototype, including the high-level electrical schematic and mechanical layout. An 18-kW prototype converter for stationary battery energy storage systems was developed and presented. The converter housing with all installed ancillary components for proper operation is also described. At the time of writing this paper, the actual converter is still in the development phase.

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Flexible Localization Method with Motion Estimation for Underwater Wireless Sensor Networks 26

Flexible Localization Method with Motion Estimation for Unde...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Ismail, A.S. Hawbani, Ammar Wang, Xingfu Aziz, Samah Abdel Zhao, Liang Saeed, Nasir School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China Zagazig University Faculty of Science Zagazig44519 Egypt School of Computer Science Shenyang Aerospace University Shenyang110136 China United Arab Emirates University Department of Electrical and Communication Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9791188428120

Due to the challenging conditions of underwater environments, such as node mobility and large-scale networks, achieving localization in large-scale mobile underwater sensor networks (UWSN) is a difficult task. This paper introduces a scheme known as the Flexible Localization Method with Mobility Estimation (FLMME) for UWSNs by utilizing the expected mobility patterns of underwater objects. FLMME performs localization hierarchically by splitting the process into anchor and ordinary node localization. Each node estimates its next mobility pattern based on previous location information, enabling estimates about its next location. Anchor nodes, holding known locations, manage the localization process to balance accuracy and error trade-offs. Extensive simulations demonstrate that FLMME reduces localization errors and hence increases localization accuracy. © 2024 Global IT Research Institute - GIRI.

关键词： Location

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Methods for Checking the Profile of the Path of Contact of Involute Gears

Methods for Checking the Profile of the Path of Contact of I...

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American Gear Manufacturers Association 2022 Fall Technical Meeting, FTM 2022

作者： Shi, Zhaoyao Sun, Yanqiang Li, Haobin Beijing Engineering Research Center of Precision Measurement Technology and Instruments Faculty of Materials and Manufacturing Beijing University of Technology China Department of Electrical and Computer Engineering Boston University United States

ISBN: (纸本)9781713862246

Involute profile and helix are the two elemental deviations for controlling the quality of gears, which are embodied in several gear accuracy standards. In this case, the main function of gear measuring instruments is designed to measure these two deviations. In fact, there is another significant characteristic curve beside the profile and helix, that is, the profile of the path of contact. During the gear hobbing, shaving, grinding and other generating machining, this curve is consistent with the machining curve on the tooth surface. During the involute helical gear transmission, this curve is also consistent with the working curve on the tooth surface. Therefore, the quality of machining and transmission of the gear can be reflected by the profile of the path of contact. However, the existing gear measuring instruments cannot measure it. In this paper, combining the forming principle of the profile of the path of contact，its theoretical model is given. Based on the existing gear measuring center, two methods for measuring the deviation of the profile of the path of contact are proposed, including the four-axis method and the three-axis method. In terms of measurement methods, the measurement of the profile of the path of contact and the involute profile is unified. The measurement practices show that the measurement and evaluation of the profile of the path of contact can be conveniently realized by the existing gear measuring instruments. As the actual working curve on the tooth surface, the profile of the path of contact is unique. In essence, it is the combined result of profile and helix, which can characterize the quality of gears. It can not only be employed for analyzing gear process errors, but also for predicting the dynamic performance of gears. Copyright ©2022.

关键词： Grinding (machining)

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