bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Learning Curve of Reaching the Planned Limb Alignment in Robotic-Arm-Assisted Total Knee Arthroplasty
Authors: Pannu Tejbir S. , Villa Jesus M. , Schultz Isaac , Barsoum Wael K. , Higuera Carlos A. , Patel Preetesh D. 
Published: December 2021

AbstractEvidence on the learning curve associated with robotic-arm-assisted total knee arthroplasty (ra-TKA) is scarce and mostly based on operative time. Thus, the objective of this study was to assess a surgeon's learning experience based on accuracy to reach planned limb alignment and its impact on surgical-characteristics, limb-alignment, and perioperative-outcomes. A retrospective chart review was conducted on a consecutive series of 204 primary ra-TKAs (patients), performed by a single surgeon in a single institution (3/7/2018-to-6/18/2019). Cumulative summation control sequential analysis was used for the assessment of the learning curve using accuracy of reaching the planned limb alignment establishing that surgeries had an initial-learning-phase, followed by a second-consolidation-phase. Baseline demographics, operative/tourniquet times, prosthesis type, and limb alignment were compared between these two phases. Length of stay, discharge disposition, complications, reoperation/readmission (90 days), and total morphine equivalents (TMEs) prescribed were compared between phases. Independent sample t-tests, and chi-squared analyses were performed. ra-TKA demonstrated a learning curve of 110 cases for reaching planned limb alignment (p = 0.012). Robotic experience resulted in significantly more proportion of knees in neutral-axis postoperatively (p = 0.035) and significant reduction in TMEs prescribed (p = 0.04). The mean operative and tourniquet time were found to be significantly lower in second-phase versus the first-phase (p for both < 0.0001). ra-TKA has a significant learning curve in clinical practice. A surgeon can reach the planned limb alignment with increased accuracy over time (110-cases). Progressive robotic learning and associated operative time efficiency can lead to significantly lower opioid consumption in patients undergoing TKA.
Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour
Authors: Yuan Jia-Hui , Chen Ya-Bo , Dou Shu-Qing , Wei Bo , Cui Huan-Qing , Song Ming-Xu , Yang Xiao-Kuo 
Published: December 2021

AbstractVoltage-driven stochastic magnetization switching in a nanomagnet has attracted more attention recently with its superiority in achieving energy-efficient artificial neuron. Here, a novel pure voltage-driven scheme with ∼27.66 aJ energy dissipation is proposed, which could rotate magnetization vector randomly using only a pair of electrodes covered on the multiferroic nanomagnet. Results show that the probability of 180° magnetization switching is examined as a sigmoid-like function of the voltage pulse width and magnitude, which can be utilized as the activation function of designed neuron. Considering the size errors of designed neuron in fabrication, it’s found that reasonable thickness and width variations cause little effect on recognition accuracy for MNIST hand-written dataset. In other words, the designed pure voltage-driven spintronic neuron could tolerate size errors. These results open a new way toward the realization of artificial neural network with low power consumption and high reliability.
Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation
Authors: Yang Zi , Chen Mingli , Kazemimoghadam Mahdieh , Ma Lin , Stojadinovic Strahinja , Timmerman Robert , Dan Tu , Wardak Zabi , Lu Weiguo , Gu Xuejun 
Published: December 2021

Abstract
Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.
Dual-Band Millimeter Wave Microstrip Patch Antenna with StubResonators for 28/38 GHz Applications
Authors: Sabek Ayman R , Ibrahim Ahmed A , Ali Wael A 
Published: December 2021

Abstract
A dual band monopole antenna with triangle stubs operated at 28/38 GHz applications is introduced. The introduced dual band antenna is used for next 5G applications. The introduced antenna is designed on a Rogers RT 4003 with height h = 0.203 mm, dielectric constant ɛr = 3.55 and over dimensions of 12×12×0.237 mm3. The simulated results show that the presented design has two bands, the first one is from 25.9 to 30.4 GHz and the second is from 36.4 to 40.2 GHz with peak gain of 4.54 dB, 4.21 dB in the first and second frequencies respectively. The simulated radiation efficiency for the first and second frequencies is 94% and 96.6%, respectively. There are some small discrepancies between simulated and measured findings due to the fabrication and measurement equipment.
A Smart Integrated Brain-Computer Interaction for Epileptic Seizure Detection
Authors: El-Khamisy A. M. , Abd El-Raoof N. M. , Youssef S. M. 
Published: December 2021

Abstract
Epilepsy is brain resulted activities which are affected by suddenly seizures which have unpredictable changes affects brain electrical functionalities. Epilepsy has a significant impact on society on the healthcare treatment, cost, responds, and patients behavior. The study has main objectives to propose accurate integrated framework for epileptic seizure detection from the pre-ictal phase of the EEG signal. Locate the connected channel lobe in region where epileptic is expected to occur. Provide automated and real-time monitoring and send warning messages to patient and epileptologist to take accurate actions before ictal occur. Enable future contribution for different Seizure features and impact. Also reduce cost, time and effort. Based on the hypothesis of entropy of EEG signals during seizure has low value if (n) of channels are detected to have seizure, then they are considered as connected neighbors in brain domain mapping, which is clear alert that patient will have a seizure ictal. This end to end framework has modules of data acquisition, pre-processing, feature extraction, pattern-matching, supports vector machines (SVM) classifier for extracted feature, in addition to monitoring and notification. The extracted features includes lower threshold, homogeneity, weighted permutation entropy, power and energy. Also identify the physiological field located inside the brain which the seizure will expected to occur. The final output results have 92% for True positive rate in addition to 95% of F1 and 98.9% of accuracy. This system has proved consistency during all its phases of seizure detection with valuable and effective support to the society.
Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach
Authors: Salama Wessam M. , Aly Moustafa H. , Elbagoury Azza M. 
Published: December 2021

Abstract
Lung cancer became a significant health problem worldwide over the past decades. This paper introduces a new generalized framework for lung cancer detection where many different strategies are explored for the classification. The ResNet50 model is applied to classify CT lung images into benign or malignant. Also, the U-Net, which is one of the most used architectures in deep learning for image segmentation, is employed to segment CT images before classification to increase system performance. Moreover, Image Size Dependent Normalization Technique (ISDNT) and Wiener filter are utilized as the preprocessing phase to enhance the images and suppress the noise. Our proposed framework which comprises preprocessing, segmentation and classification phases, is applied on two databases: Lung Nodule Analysis 2016 (Luna 16) and National Lung Screening Trial (NLST). Data augmentation technique is applied to solve the problem of lung CT images deficiency, and consequently, the overfitting of deep models will be avoided. The classification results show that the preprocessing for the CT lung image as the input for ResNet50-U-Net hybrid model achieves the best performance. The proposed model achieves 98.98% accuracy (ACC), 98.65% area under the ROC curve (AUC), 98.99% sensitivity (Se), 98.43% precision (Pr), 98.86% F1- score and 1.9876 s computational time.
Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model
Authors: Moataz Laila , Salama Gouda I. , Abd Elazeem Mohamed H. 
Published: December 2021

Abstract
Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.
Spinal Muscle Atrophy Disease Modelling as Bayesian Network
Authors: Helal Mohammed Ezzat , Helal Manal Ezzat , Fahmy Professor Sherif Fadel 
Published: December 2021

Abstract
We investigate the molecular gene expressions studies and public databases for disease modelling using Probabilistic Graphical Models and Bayesian Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study results is modelled and analyzed. The genes up and down-regulated in two stages of the disease development are linked to prior knowledge published in the public domain and co-expressions network is created and analyzed. The Molecular Pathways triggered by these genes are identified. The Bayesian inference posteriors distributions are estimated using a variational analytical algorithm and a Markov chain Monte Carlo sampling algorithm. Assumptions, limitations and possible future work are concluded.
Neurological Disorder Detection of Brain Abnormal Activities Using a New Enhanced Computer-aided Model
Authors: Shoieb D , Youssef S 
Published: December 2021

Abstract
In the field of neurodevelopmental disorders, Autism Spectrum Disorders (ASD) are recognized as one of the dramatically increased etiologically and clinically heterogeneous diseases. Although, increasing the number of children who have difficulties in communication or suffer from sudden malfunction of the brain, the current diagnostic approaches for those kind of disease are time-consuming and are mainly based on clinical interviews. In this paper, a new enhanced diagnostic model is introduced addressing many of the challenges which threats the analysis of Electroencephalography (EEG) signals. A preprocessing stage is proposed to choose the key segment of EEG channel and remove the artifacts in the EEG signals to enhance their quality. The proposed model uses a set of discriminative features based on discrete wavelet transform (DWT) such as skewness, standard division, shannon entropy and relative wave energy. Also, extracting cross correction between brain regions to detect abnormal connectivity and synchronization. Two EEG datasets are used to verify the accuracy of the proposed model. The fusion of two EEG dataset helps in building a more generalized mode to diagnose epilepsy and ASD. In the fused dataset, the combination of the mentioned features and Random Forest have produced a very promising diagnosis result with minimum diagnostic time, with an average accuracy equals to 96.78%. The proposed model obtained better classification accuracy compared to the existing methods.
Intelligent High Payload Audio Watermarking Algorithm Using Colour Image in DWT-SVD Domain
Authors: Elshazly A R , Nasr Mohamed E. , Fouad M M , Abdel-Samie Fathi E. 
Published: December 2021

Abstract
Copyright protection and ownership verification of digital audio tracks have become increasingly important to be enabled by digital watermarking techniques. A novel high payload intelligent audio watermarking scheme with RGB color watermark image is proposed in this paper. The color watermark image is encrypted using Arnold chaotic map and passed through an adaptive scaling filter to scale the image to match the required payload. The encoding process is performed on the scaled encrypted version of the watermark image. A portion of the audio signal is used to embed a synchronization code and the other one is decomposed into short frames. These frames are processed with a two-level discrete wavelet transform (DWT), followed by a singular value decomposition (SVD) process on the approximation coefficients. The encoded watermark is inserted into the diagonal matrix using quantization index modulation (QIM). The inverse process of SVD and DWT is applied to obtain the marked audio signal. Blind extraction of the hidden information from the marked audio signal is performed in the reverse order of the embedding process. Experiments show that security, high payload, transparency and imperceptibility of the algorithm are satisfied. The robustness against several kinds of audio signal processing attacks is shown. Performance evaluation tests with SNR, BER, and FSIM are conducted.
Development of Smart 3D Object Detection using LiDAR Cloud-Data Collection
Authors: Abd Elhamied Essam M. , Youssef Sherin M. 
Published: December 2021

Abstract
Smart cities are made up of autonomous vehicle and they communicate and interact with their environment and require high precision computer vision to maintain driver and pedestrian safety. This paper presents a cost-efficient, non-intrusive and easy to use method for collecting data traffic counts using LiDAR technology. The proposed method incorporates a LiDAR sensor, a Convolutional Neural Network (CNN) and a Hybrid SVM into a single traffic counting framework. As the technology is economical and readily accessible, LiDAR is adopted. The distance data obtained are translated into the signals. Due to the difficulty of urban scenes, automatic detection of objects from remotely sensed data within urban areas is difficult. While recent advances in computer vision have shown that CNNs are very suitable for this task, the design and training of CNNs of this kind remained demanding and time consuming, given the challenge of collecting a large and well-annotated dataset and the specificity of every task. Hybrid SVM is a supervised data classification and regression machine learning tool. In the methodology the Hybrid SVM is used in detection and non-detection cases of highly complex distance data points obtained from the sensor. In order to examine the performance of the proposed method, the test is carried out in three different locations in Alexandria, Egypt. The results of tests show that the pro-imposed method achieves acceptable results in vehicle collection, which results in a precision of 85–89%. The exactness of the method proposed is determined by the colour of a vehicle’s external surface.
Intelligent Control for Pressurizer System in Nuclear Power Plants
Authors: Sheta A A , Ali E H , Fikry R M , Mahmoud T A , El-Araby S M , Mahmoud M I 
Published: December 2021

Abstract
Fine control of output power for nuclear power plants is the essential goal for safe operation. In this work, a Fuzzy analytical proportional-integral-derivative (FPID) controller with different configurations is designed to adjust and control the pressure of the PZR system. The stability analysis of the FPID controller with variable gains is established, and conditions for bounded-input bounded-output stability conditions (BIBO) are derived using the small gain theory. Two scenarios are applied for evaluating the dynamic response of applied controllers. In addition, performance indices are compared between the PZR model and data measured from the PCtran VVER-1200 simulator. Finally, a simulation platform is developed for MATLAB / Simulink to implement the three-region nonlinear non-equilibrium PZR model and the designed pressure controllers. The analysis and evaluation results showed good durability of the designed controllers and satisfactory performance of the control. These results further show that the nonlinear PZR model is accurate, feasible, and valuable for dynamic simulation and control unit design.
Investigation on Online DGA Monitoring to Determine Transformer Health Index Using Machine Learning
Authors: Solehin Shamsudin M , Yakub Fitri , Ibrahim Shapiai M , Mohmad Azlan , Amirah Abd Hamid N 
Published: December 2021

Abstract
The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.
Single loop PID controller design based on optimization algorithms for parallelly connected dc-dc converters
Authors: Allam Allam M. , Ibrahim A.S. , Nabil Essam 
Published: December 2021

Abstract
This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.
Aeroacoustics behavioral study of Savonius wind turbine
Authors: Malainine Maymouna , Khaled Amany , Shabaan Sameh M 
Published: December 2021

Abstract
Vertical Axis Wind Turbines (VAWTs) are appropriate for use in populated areas. If VAWTs were installed at residential areas, the generated aerodynamic noise can be harmful in a way or another. Therefore, in the present study, the aero-acoustics of the conventional Savonius Wind turbine was investigated using Computational Fluid Dynamics (CFD). Both the Unsteady Reynolds-averaged Navier-Stokes (URANS) equations and impermeable Ffowcs Wiliams and Hawkings (FW-H) equation were simultaneously solved. The effect of speed ratio was also studied. The results indicate that; the pressure is inversely proportional to the speed ratio. Additionally, the velocity has been increased due to the increase of the tip speed ratio. Finally, it has improved that for the majority of receivers, the overall sound level increases with increasing speed ratio.
Automated process control system of Jaw crusher production
Authors: Elgendi Elbadr O , Shawki Khaled 
Published: December 2021

Abstract
Rocks go through a series of crushing stages to produce the final aggregate size which suit customer’s needs. Aggregate crushers suffer from many problems in reality that affect its production processes badly and may lead to its stoppage. These problems could be unchangeable feed rate, hidden steel part between rocks and wastage from storing final aggregate stockpile on the ground. The purpose of this research is to develop an automated control system for Jaw crusher production process as a solution for the real specified problems to get more productive aggregate production. First a 3D model which mimic exactly the real crushing plant was built to test the proposed system. Then a C++ code was programmed to control the proposed system components such as motor, sensors and LCD screens. Thereafter, the aggregate flow simulation program (AggFlow) is utilized to simulate the proposed system results. Cost analysis calculations were performed to explore the impact of proposed system on the production process cost and time. Finally, the results confirmed that the proposed system enhance and increase the crusher production process. As it shows increase in the crusher automatic control, the net profit and the feed rate while decrease in the final product waste.