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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

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Computers, Materials & Continua 2023年第1期74卷 379-397页

作者： Edgar Scavino Mohd Amiruddin Abd Rahman zahid farid Department of Physics Faculty of ScienceUniversiti Putra MalaysiaUPM Serdang43400Malaysia Department of Electrical Engineering Abasyn UniversityKhyber-PakhtunkhwaPakistan

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units,in large indoor spaces demands a precise knowledge of their *** like WiFi and Bluetooth,despite their low-cost and availability,are sensitive to signal noise and fading *** these reasons,a hybrid approach,which uses two different signal sources,has proven to be more resilient and accurate for the positioning determination in indoor ***,this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning,using Received Signal Strength information from available Wireless Local Area Network access points,together with the Wireless Sensor Networks *** signals were recorded on a regular grid of anchor points,covering the research *** optimization was performed by relative signal weighting,to minimize the average positioning error over the research *** optimization process was conducted using a standard Quantum Particle Swarm Optimization,while the position error estimate for all given sets of weighted signals was performed using aMultilayer Perceptron(MLP)neural *** to our previous research works,the MLP architecture was improved to three hidden layers and its learning parameters were finely *** experimental results led to the 20%reduction of the positioning error when a suitable set of signal weights was calculated in the optimization *** final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.

关键词： QPSO indoor localization fingerprinting neural networks WiFi WSN

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Estimation of infiltration models parameters and their comparison to simulate the onsite soil infiltration characteristics

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International Journal of Agricultural and Biological Engineering 2019年第3期12卷 84-91页

作者： Hafiz Umar farid zahid Mahmood-Khan Ijaz Ahmad Aamir Shakoor Muhammad Naveed Anjum Muhammad Mazhar Iqbal Muhammad Mubeen Muhammad Asghar Department of Agricultural Engineering Bahauddin Zakariya UniversityMultan-Pakistan Center of Excellence in Water Resources Engineering(CEWRE) University of Engineering and TechnologyLahore-Pakistan Division of Hydrology Water-Land Resources in Cold and Arid Regions Cold and Arid Region Environmental and Engineering InstituteChinese Academy of SciencesLanzhou 730000China Graduate School of Water Resources Sungkyunkwan UniversitySuwon-si 2066Korea Department of Environmental Sciences COMSATS Institute of Information TechnologyVehari-Pakistan

Detailed knowledge about soil characteristics and site-specific final steady infiltration rate could help to increase the irrigation water use efficiency and decrease water losses in agricultural *** experiments were conducted on Agricultural Research Farm of Bahauddin Zakariya University,Multan,Pakistan during *** cumulative infiltration depth was measured using double ring infiltrometer at selected six points of the study *** commonly used infiltration models such as Kostikov’s,Philip’s and Horton’s were fitted to the field infiltration data for determination of model parameters and to find the best fit model for the study ***’s infiltration model’s parameters such as empirical constant‘c’and infiltration decay constants‘k’were obtained in the ranges of 0.140-0.290 and 0.307-0.433,***’s infiltration model’s parameters such as sorptivity‘S’and conductivity constant‘A’were found in the ranges of 0.167-0.288 cm/min1/2 and-0.001 to-0.009 cm/min,***,the Horton’s model’s‘parameter‘k’was obtained in the range of-1.619 *** value of infiltration capacity at onset of infiltration(fo)was obtained as 1.744 to 3.491 for all the six *** analysis showed that the infiltration models using the estimated parameters have satisfactory prediction capability at all the selected ***’s model provided the lowest mean values for RMSE(0.235)and highest mean values for ME(94%);and the lowest mean values for MPD(0.127).This indicated that infiltration can be well-described by the Horton’s model at the selected site.

关键词： infiltration characteristics infiltration models parameters estimation perdition capability

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