MyMedR

Fulltext A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems

Wang S, Liu Q, Liu Y, Jia H, Abualigah L, Zheng R, et al.

Comput Intell Neurosci, 2021;2021:6379469.

PMID: 34531910 DOI: 10.1155/2021/6379469

Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms' advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique's practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation.

Matched MeSH terms: Algorithms*

Fulltext A Hybridised Intelligent Technique for the diagnosis of medical diseases

Abdu Masanawa Sagir, Saratha Sathasivam

Pertanika Journal of Science & Technology, 2017;25(2):425-440.

MyJurnal

Medical diagnosis is the process of determining which disease or medical condition explains a person’s determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient’s characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine’s (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior
performance when compared to some other related existing methods.

Matched MeSH terms: Algorithms

A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Masuyama N, Loo CK, Wermter S

Int J Neural Syst, 2019 Jun;29(5):1850052.

PMID: 30764724 DOI: 10.1142/S0129065718500521

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.

Matched MeSH terms: Algorithms*

Fulltext A Low Cost and Portable Microwave Imaging System for Breast Tumor Detection Using UWB Directional Antenna array

Islam MT, Mahmud MZ, Islam MT, Kibria S, Samsuzzaman M

Sci Rep, 2019 10 29;9(1):15491.

PMID: 31664056 DOI: 10.1038/s41598-019-51620-z

Globally, breast cancer is a major reason for female mortality. Due to the limitations of current clinical imaging, the researchers are encouraged to explore alternative and complementary tools to available techniques to detect the breast tumor in an earlier stage. This article outlines a new, portable, and low-cost microwave imaging (MWI) system using an iterative enhancing technique for breast imaging. A compact side slotted tapered slot antenna is designed for microwave imaging. The radiating fins of tapered slot antenna are modified by etching nine rectangular side slots. The irregular slots on the radiating fins enhance the electrical length as well as produce strong directive radiation due to the suppression of induced surface currents that radiate vertically at the outer edges of the radiating arms with end-fire direction. It has remarkable effects on efficiency and gain. With the addition of slots, the side-lobe levels are reduced, the gain of the main-lobe is increased and corrects the squint effects simultaneously, thus improving the characteristics of the radiation. For experimental validation, a heterogeneous breast phantom was developed that contains dielectric properties identical to real breast tissues with the inclusion of tumors. An alternative PC controlled and microcontroller-based mechanical MWI system is designed and developed to collect the antenna scattering signal. The radiated backscattered signals from the targeted area of the human body are analyzed to reveal the changes in dielectric properties in tissues. The dielectric constants of tumorous cells are higher than that of normal tissues due to their higher water content. The remarkable deviation of the scattered field is processed by using newly proposed Iteratively Corrected Delay and Sum (IC-DAS) algorithm and the reconstruction of the image of the phantom interior is done. The developed UWB (Ultra-Wideband) antenna based MWI has been able to perform the detection of tumorous cells in breast phantom that can pave the way to saving lives.

Matched MeSH terms: Algorithms

Fulltext A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping

Kzar AA, Mat Jafri MZ, Mutter KN, Syahreza S

Int J Environ Res Public Health, 2016 Jan;13(1).

PMID: 26729148 DOI: 10.3390/ijerph13010092

Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).

Matched MeSH terms: Algorithms

Fulltext A Multiobjective, Lion Mating Optimization Inspired Routing Protocol for Wireless Body Area Sensor Network Based Healthcare Applications

Faheem M, Butt RA, Raza B, Alquhayz H, Abbas MZ, Ngadi MA, et al.

Sensors (Basel), 2019 Nov 20;19(23).

PMID: 31757104 DOI: 10.3390/s19235072

The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications.

Matched MeSH terms: Algorithms

A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks

Chong SY, Tiňo P, He J, Yao X

Evol Comput, 2019;27(2):195-228.

PMID: 29155606 DOI: 10.1162/evco_a_00218

Studying coevolutionary systems in the context of simplified models (i.e., games with pairwise interactions between coevolving solutions modeled as self plays) remains an open challenge since the rich underlying structures associated with pairwise-comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problems that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modeled as a specific type of Markov chains-random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provides the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled manner.

Matched MeSH terms: Algorithms*

Fulltext A New Method for a Piezoelectric Energy Harvesting System Using a Backtracking Search Algorithm-Based PI Voltage Controller

Sarker MR, Mohamed A, Mohamed R

Micromachines (Basel), 2016 Sep 23;7(10).

PMID: 30404344 DOI: 10.3390/mi7100171

This paper presents a new method for a vibration-based piezoelectric energy harvesting system using a backtracking search algorithm (BSA)-based proportional-integral (PI) voltage controller. This technique eliminates the exhaustive conventional trial-and-error procedure for obtaining optimized parameter values of proportional gain (Kp), and integral gain (Ki) for PI voltage controllers. The generated estimate values of Kp and Ki are executed in the PI voltage controller that is developed through the BSA optimization technique. In this study, mean absolute error (MAE) is used as an objective function to minimize output error for a piezoelectric energy harvesting system (PEHS). The model for the PEHS is designed and analyzed using the BSA optimization technique. The BSA-based PI voltage controller of the PEHS produces a significant improvement in minimizing the output error of the converter and a robust, regulated pulse-width modulation (PWM) signal to convert a MOSFET switch, with the best response in terms of rise time and settling time under various load conditions.

Matched MeSH terms: Algorithms

Fulltext A New Random Walk for Replica Detection in WSNs

Aalsalem MY, Khan WZ, Saad NM, Hossain MS, Atiquzzaman M, Khan MK

PLoS One, 2016;11(7):e0158072.

PMID: 27409082 DOI: 10.1371/journal.pone.0158072

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.

Matched MeSH terms: Algorithms

Fulltext A Novel Approach for Classifying Native Chinese and Malay Speaking Persons According to Cortical Auditory Evoked Responses

Ibrahim IA, Ting HN, Moghavvemi M

J Int Adv Otol, 2019 Apr;15(1):87-93.

PMID: 30924771 DOI: 10.5152/iao.2019.4553

OBJECTIVES: This study uses a new approach for classifying the human ethnicity according to the auditory brain responses (electroencephalography [EEG] signals) with a high level of accuracy. Moreover, the study presents three different algorithms used to classify the human ethnicity using auditory brain responses. The algorithms were tested on Malays and Chinese as a case study.

MATERIALS AND METHODS: The EEG signal was used as a brain response signal, which was evoked by two auditory stimuli (Tones and Consonant Vowels stimulus). The study was carried out on Malaysians (Malay and Chinese) with normal hearing and with hearing loss. A ranking process for the subjects' EEG data and the nonlinear features was used to obtain the maximum classification accuracy.

RESULTS: The study formulated the classification of Normal Hearing Ethnicity Index and Sensorineural Hearing Loss Ethnicity Index. These indices classified the human ethnicity according to brain auditory responses by using numerical values of response signal features. Three classification algorithms were used to verify the human ethnicity. Support Vector Machine (SVM) classified the human ethnicity with an accuracy of 90% in the cases of normal hearing and sensorineural hearing loss (SNHL); the SVM classified with an accuracy of 84%.

CONCLUSION: The classification indices categorized or separated the human ethnicity in both hearing cases of normal hearing and SNHL with high accuracy. The SVM classifier provided a good accuracy in the classification of the auditory brain responses. The proposed indices might constitute valuable tools for the classification of the brain responses according to the human ethnicity.

Matched MeSH terms: Algorithms

A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images

Vinothini R, Niranjana G, Yakub F

J Digit Imaging, 2023 Dec;36(6):2480-2493.

PMID: 37491543 DOI: 10.1007/s10278-023-00852-7

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.

Matched MeSH terms: Algorithms

Fulltext A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

Hussain S, Mustafa MW, Al-Shqeerat KHA, Saeed F, Al-Rimy BAS

Sensors (Basel), 2021 Dec 17;21(24).

PMID: 34960516 DOI: 10.3390/s21248423

This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

Matched MeSH terms: Algorithms*

Fulltext A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

Devan PAM, Ibrahim R, Omar M, Bingi K, Abdulrab H

Sensors (Basel), 2023 Jul 07;23(13).

PMID: 37448072 DOI: 10.3390/s23136224

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.

Matched MeSH terms: Algorithms*

Fulltext A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

Goudarzi S, Haslina Hassan W, Abdalla Hashim AH, Soleymani SA, Anisi MH, Zakaria OM

PLoS One, 2016;11(7):e0151355.

PMID: 27438600 DOI: 10.1371/journal.pone.0151355

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

Matched MeSH terms: Algorithms*

Fulltext A Planar Ultrawideband Patch Antenna Array for Microwave Breast Tumor Detection

Hossain A, Islam MT, Islam MT, Chowdhury MEH, Rmili H, Samsuzzaman M

Materials (Basel), 2020 Nov 02;13(21).

PMID: 33147702 DOI: 10.3390/ma13214918

In this paper, a compact planar ultrawideband (UWB) antenna and an antenna array setup for microwave breast imaging are presented. The proposed antenna is constructed with a slotted semicircular-shaped patch and partial trapezoidal ground. It is compact in dimension: 0.30λ × 0.31λ × 0.011λ, where λ is the wavelength of the lowest operating frequency. For design purposes, several parameters are assumed and optimized to achieve better performance. The prototype is applied in the breast imaging scheme over the UWB frequency range 3.10-10.60 GHz. However, the antenna achieves an operating bandwidth of 8.70 GHz (2.30-11.00 GHz) for the reflection coefficient under-10 dB with decent impedance matching, 5.80 dBi of maximum gain with steady radiation pattern. The antenna provides a fidelity factor (FF) of 82% and 81% for face-to-face and side-by-side setups, respectively, which specifies the directionality and minor variation of the received pulses. The antenna is fabricated and measured to evaluate the antenna characteristics. A 16-antenna array-based configuration is considered to measure the backscattering signal of the breast phantom where one antenna acts as transmitter, and 15 of them receive the scattered signals. The data is taken in both the configuration of the phantom with and without the tumor inside. Later, the Iteratively Corrected Delay and Sum (IC-DAS) image reconstructed algorithm was used to identify the tumor in the breast phantom. Finally, the reconstructed images from the analysis and processing of the backscattering signal by the algorithm are illustrated to verify the imaging performance.

Matched MeSH terms: Algorithms

A Positive (+ve) Postoperative Upper Instrumented Vertebra Tilt Angle (≥0°) Significantly Increases the Risk of Medial Shoulder and Neck Imbalance in Lenke 1 and 2 Adolescent Idiopathic Scoliosis Patients

Kwan MK, Lee SY, Ch'ng PY, Chung WH, Chiu CK, Chan CYW

Spine (Phila Pa 1976), 2020 Jun 15;45(12):E694-E703.

PMID: 32032325 DOI: 10.1097/BRS.0000000000003407

STUDY DESIGN: Retrospective study.

OBJECTIVE: To investigate the relationship between a +ve postoperative Upper Instrumented Vertebra (UIV) (≥0°) tilt angle and the risk of medial shoulder/neck and lateral shoulder imbalance among Lenke 1 and 2 Adolescent Idiopathic Scoliosis (AIS) patients following Posterior Spinal Fusion.

SUMMARY OF BACKGROUND DATA: Current UIV selection strategy has poor correlation with postoperative shoulder balance. The relationship between a +ve postoperative UIV tilt angle and the risk of postoperative shoulder and neck imbalance was unknown.

METHODS: One hundred thirty-six Lenke 1 and 2 AIS patients with minimum 2 years follow-up were recruited. For medial shoulder and neck balance, patients were categorized into positive (+ve) imbalance (≥+4°), balanced, or negative (-ve) imbalance (≤-4°) groups based on T1 tilt angle/Cervical Axis measurement. For lateral shoulder balance, patients were classified into +ve imbalance (≥+3°) balanced, and -ve imbalance (≤-3°) groups based on Clavicle Angle (Cla-A) measurement. Linear regression analysis identified the predictive factors for shoulder/neck imbalance. Logistic regression analysis calculated the odds ratio of shoulder/neck imbalance for patients with +ve postoperative UIV tilt angle.

RESULTS: Postoperative UIV tilt angle and preoperative T1 tilt angle were predictive of +ve medial shoulder imbalance. Postoperative UIV tilt angle and postoperative PT correction were predictive of +ve neck imbalance. Approximately 51.6% of patients with +ve medial shoulder imbalance had +ve postoperative UIV tilt angle. Patients with +ve postoperative UIV tilt angle had 14.9 times increased odds of developing +ve medial shoulder imbalance and 3.3 times increased odds of developing +ve neck imbalance. Postoperative UIV tilt angle did not predict lateral shoulder imbalance.

CONCLUSION: Patients with +ve postoperative UIV tilt angle had 14.9 times increased odds of developing +ve medial shoulder imbalance (T1 tilt angle ≥+4°) and 3.3 times increased odds of developing +ve neck imbalance (cervical axis ≥+4°).

LEVEL OF EVIDENCE: 4.

Matched MeSH terms: Algorithms

Fulltext A Predictive and Preventive Model for Onset of Alzheimer's Disease

Singhania U, Tripathy B, Hasan MK, Anumbe NC, Alboaneen D, Ahmed FRA, et al.

Front Public Health, 2021;9:751536.

PMID: 34708019 DOI: 10.3389/fpubh.2021.751536

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.

Matched MeSH terms: Algorithms

Fulltext A Promising Polymer Blend Electrolytes Based on Chitosan: Methyl Cellulose for EDLC Application with High Specific Capacitance and Energy Density

Aziz SB, Hamsan MH, Abdullah RM, Kadir MFZ

Molecules, 2019 Jul 09;24(13).

PMID: 31323966 DOI: 10.3390/molecules24132503

In the present work, promising proton conducting solid polymer blend electrolytes (SPBEs) composed of chitosan (CS) and methylcellulose (MC) were prepared for electrochemical double-layer capacitor (EDLC) application with a high specific capacitance and energy density. The change in intensity and the broad nature of the XRD pattern of doped samples compared to pure CS:MC system evidencedthe amorphous character of the electrolyte samples. The morphology of the samples in FESEM images supported the amorphous behavior of the solid electrolyte films. The results of impedance and Bode plotindicate that the bulk resistance decreasedwith increasing salt concentration. The highest DC conductivity was found to be 2.81 × 10-3 S/cm. The electrical equivalent circuit (EEC) model was conducted for selected samples to explain the complete picture of the electrical properties.The performance of EDLC cells was examined at room temperature by electrochemical techniques, such as impedance spectroscopy, cyclic voltammetry (CV) and constant current charge-discharge techniques. It was found that the studied samples exhibit a very good performance as electrolyte for EDLC applications. Ions were found to be the dominant charge carriers in the polymer electrolyte. The ion transference number (tion) was found to be 0.84 while 0.16 for electron transference number (tel). Through investigation of linear sweep voltammetry (LSV), the CS:MC:NH4SCN system was found to be electrochemically stable up to 1.8 V. The CV plot revealed no redox peak, indicating the occurrence of charge double-layer at the surface of activated carbon electrodes. Specific capacitance (Cspe) for the fabricated EDLC was calculated using CV plot and charge-discharge analyses. It was found to be 66.3 F g-1 and 69.9 F g-1 (at thefirst cycle), respectively. Equivalent series resistance (Resr) of the EDLC was also identified, ranging from 50.0 to 150.0 Ω. Finally, energy density (Ed) was stabilized to anaverage of 8.63 Wh kg-1 from the 10th cycle to the 100th cycle. The first cycle obtained power density (Pd) of 1666.6 W kg-1 and then itdropped to 747.0 W kg-1 at the 50th cycle and continued to drop to 555.5 W kg-1 as the EDLC completed 100 cycles.

Matched MeSH terms: Algorithms

A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

Al-Fakih AM, Algamal ZY, Lee MH, Aziz M, Ali HTM

SAR QSAR Environ Res, 2019 Jun;30(6):403-416.

PMID: 31122062 DOI: 10.1080/1062936X.2019.1607899

Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter,
μ
, is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on

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, Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher

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compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.

Matched MeSH terms: Algorithms

Fulltext A ROM-less direct digital frequency synthesizer based on hybrid polynomial approximation

Omran QK, Islam MT, Misran N, Faruque MR

ScientificWorldJournal, 2014;2014:812576.

PMID: 24892092 DOI: 10.1155/2014/812576

In this paper, a novel design approach for a phase to sinusoid amplitude converter (PSAC) has been investigated. Two segments have been used to approximate the first sine quadrant. A first linear segment is used to fit the region near the zero point, while a second fourth-order parabolic segment is used to approximate the rest of the sine curve. The phase sample, where the polynomial changed, was chosen in such a way as to achieve the maximum spurious free dynamic range (SFDR). The invented direct digital frequency synthesizer (DDFS) has been encoded in VHDL and post simulation was carried out. The synthesized architecture exhibits a promising result of 90 dBc SFDR. The targeted structure is expected to show advantages for perceptible reduction of hardware resources and power consumption as well as high clock speeds.

Matched MeSH terms: Algorithms

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