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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Inter-well internal resonance analysis of rectangular asymmetric cross-ply bistable composite laminated cantilever shell under transverse foundation excitation

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Applied Mathematics and Mechanics(English Edition) 2024年第8期45卷 1353-1370页

作者： Lele REN Wei ZHANG Yufei ZHANG State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China Department of Mechanics Guangxi UniversityNanning 530004China Beijing Key Laboratory of Nonlinear Vibrations and Strength of Mechanical Structures College of Mechanical EngineeringBeijing University of TechnologyBeijing 100124China

The chaotic dynamic snap-through and complex nonlinear vibrations are investigated in a rectangular asymmetric cross-ply bistable composite laminated cantilever shell,in cases of 1:2 inter-well internal resonance and primary *** transverse foundation excitation is applied to the fixed end of the structure,and the other end is in a free *** first-order approximate multiple scales method is employed to perform the perturbation analysis on the dimensionless two-degree-of-freedom ordinary differential motion control *** four-dimensional averaged equations are derived in both polar and rectangular coordinate *** from the obtained frequency-amplitude and force-amplitude response curves,a detailed analysis is conducted to examine the impacts of excitation amplitude,damping coefficient,and tuning parameter on the nonlinear internal resonance characteristics of the *** nonlinear softening characteristic is exhibited in the upper stable-state,while the lower stable-state demonstrates the softening and linearity *** simulation is carried out using the fourth-order Runge-Kutta method,and a series of nonlinear response curves are *** the excitation amplitude further elucidates the global bifurcation and chaotic dynamic snap-through characteristics of the bistable cantilever shell.

关键词： bistable composite laminated cantilever shell inter-well internal resonance primary resonance chaotic dynamic snap-through complex nonlinear vibration

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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An experimental and numerical approach for optimization of internal and external corrugations with various geometries in cylindrical thin-walled concertina energy absorbers under quasi-static axial load

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Mechanics of Advanced Materials and Structures 2024年第25期31卷 6628-6642页

作者： Rahpeima, Reza Nateghi Boroujeni, Iman Nia, Ali Alavi Graduate institute of biomedical electronics and bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Department of Mechanical Engineering Tarbiat Modares University Tehran Iran Department of Mechanical Engineering Bu-Ali Sina University Hamedan Iran

Thin-walled tubes have been extensively used as energy absorbers in many engineering structures that are under dynamic or quasi-static loads. In this article, the effect of corrugation and its geometry on the collapse characteristics of aluminum concertina tubes under quasi-static axial load is investigated both experimentally and numerically. The numerical simulations were performed with the use of LS-Dyna commercial software. Then, several chosen samples were tested experimentally to verify the results of the simulation. Forty different samples with four different corrugation geometries including singly isosceles triangular corrugation, continual isosceles triangular corrugation, continual scalene triangular corrugation, and continual semi-circular corrugation, and each with 10 different size dimensions were simulated. Among all of these simulations, six of them, each with two replicates were subjected to experimental tests. Experimental examples are aluminum cylindrical thin-walled tubes with the same thickness, height, and diameter that corrugations with different geometries are applied to them by the use of the cold rolling method. Finally, to describe the findings in more theoretical terms, a comparison between our obtained results and earlier theoretical investigations is made. The results show a significant influence of corrugations application on the improvement of the collapse characteristics of the cylindrical tubes so that the collapse style of intact tubes can be controlled by applying corrugations. The results show that by increasing the amplitude and number of corrugations, the absorbed energy, and the initial maximum force decrease. It is also found that among the different corrugation geometries, the corrugations with singly isosceles triangular geometry have the highest energy absorption as well as the lowest initial maximum force. © 2023 Taylor & Francis Group, LLC.

关键词： Geometry

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks

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SN computer science 2024年第8期5卷 1-14页

作者： Antony Vigil, M.S. Maheswari, K. Minu, M.S. Kulkarni, Gururaj L. Chandra Sekhar Reddy, L. Satishkumar, P. Haldar, Barun Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India Department of Computer Science and Engineering CMR Technical Campus Telangana Kandlakoya Hyderabad India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Shamshabad India Department of Computer Science and Engineering (Data Science) CMR College of Engineering & amp Technology Kandlakoya(V) Hyderabad Medchal India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Mechanical and Industrial Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

In many different sectors, the safety and dependability of welded structures are crucially dependent on the accurate identification of weld defects. To address this, we present a novel method for categorizing weld defects named as Pelican Optimization-based convolutional Bi-Long Short-Term Memory Networks (POCBi-LSTM). With this integrated strategy, weld defect categorization is intended to be more accurate and effective. To train and assess a suggested classification algorithm, a collection of 220 samples with 5various types of weld faults is utilized. The study's technique comprised denoising with a Modified Median Filter (MMF) for noise reduction and binary histogram equalization (BHE) for image enhancement. The Scale Invariant Feature Transform (SIFT) was used to extract features. The outcomes demonstrate that the proposed strategy performs greater than conventional methods and delivers superior recognition accuracy, precision, recall, and F1 scores of 92.9%, 94.5%, 89.5%, and 92.8%, respectively. Experiment findings demonstrate the effectiveness and advantage of the recommended approach in classifying weld faults. Through the use of an integrated strategy, it is hoped to increase the dependability and effectiveness of weld defect categorization by handling local pattern recognition and long-term interdependence efficiently. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Modified median filter Pelican Optimization-Based convolutional Bi-Long Short Term Memory Networks (POCBi-LSTM) Weld defects Weld structures

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Predictive Analysis of Liver Disease Using Random Forest Machine Learning Algorithm 1

Predictive Analysis of Liver Disease Using Random Forest Mac...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vetrithangam, D. Kumar, Nallabariki Praveen Arya, Neha Tiwari, Amrita Raju, G. Jaya Srinivas, Gollapalli Veera Satya Chandigarh University Department of Computer Science & Engineering Mohali India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Aditya College of Engineering and Technology Department of Mechanical Engineering Surampalem India

ISBN: (纸本)9798350376517

The proposed system's objective is to improve the performance of diagnosing liver diseases through machine learning by using the Random Forest algorithm. Such systems accommodate a detailed database that comprises of several parameters of the patient's health status, through which important factors that cause liver disease may be determined. The analysis utilizes Random Forest, a feature selection technique that boosts the efficacy of the model's results in determining the outcomes of liver diseases with accuracy and recall. The training is conducted to reduce the component's error rate as well as to determine the ability of the system to perform complex tasks. Likewise, the proposed solution also focuses on the easy-to-use front-end for the target groups - the healthcare organizations - so the solution perfectly fits into their day-to-day work. In sum, this system is designed to become an innovative, cutting edge, and evidence-based tool for the notification and prevention of the liver disease in its early stage so as to enhance patients' outcomes and make more efficient use of the health care facilities. © 2024 IEEE.

关键词： Contrastive Learning

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A Large-Stroke 3-DOF Micromirror With Novel Lorentz Force-Based Actuators Utilizing Metallic Glass Thin Film

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Journal of Microelectromechanical systems 2024年第1期33卷 46-53页

作者： Ou, Chuan-Hui Van Toan, Nguyen Tsai, Yao-Chuan Voiculescu, Ioana Toda, Masaya Ono, Takahito Tohoku University Department of Mechanical Systems Engineering Sendai980-8570 Japan National Chung Hsing University Department of Bio-Industrial Mechatronics Engineering Taichung City40227 Taiwan The City College of New York Department of Mechanical Engineering New YorkNY10031 United States Tohoku University Micro System Integration Center Department of Mechanical Systems Engineering Sendai980-8570 Japan

An electromagnetic tip-tilt-piston micromirror with a large stroke is presented. This research introduced a novel actuation structure, based on a spring made from conductive metallic glass with excellent mechanical properties. For the first time, two functional elements, an electromagnetic actuation element and a mechanical support structure, were successfully integrated into a single-layer spring, made of metallic glass. With this novel actuator, the performance of stroke and angle controllability is remarkably improved. The fabricated device can achieve a 418 ~\mu \text{m} static stroke in upward direction. Furthermore, the tilting angles of the micromirror can be controlled with maximum accuracy of 0.05°/mA. The device is robust and has miniature dimensions, comparable or smaller, than existing electromagnetic micromirror dimensions published in literature. The displacement values of the micromirror are larger, compared to state-of-the-art electromagnetic micromirrors, which usually have strokes under 300 ~\mu \text{m} and do not have angle control mechanism. In spectrometer applications, the micromirror with a large stroke and controllable angle allows spectrometers to achieve higher resolution. These special qualities of the micromirror, will provide efficient, and reliable spatial light modulation required for interferometer applications. [2023-0104] © 1992-2012 IEEE.

关键词： Interferometers

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Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

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Big Data Mining and Analytics 2023年第2期6卷 170-184页

作者： Mohammed G.Albayati Jalal Faraj Amy Thompson Prathamesh Patil Ravi Gorthala Sanguthevar Rajasekaran Department of Mechanical Engineering School of EngineeringUniversity of ConnecticutStorrsCT 06269USA Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269USA Department of Mechanical and Industrial Engineering Tagliatela College of EngineeringUniversity of New HavenWest HavenCT 06516USA.

Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over ***,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure *** is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on *** available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system ***,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the *** this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive *** study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in *** paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.

关键词： semi-supervised machine learning fault classification fault detection and diagnostics heating,ventilation,and air-conditioning data-driven modeling energy efficiency

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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