Amplitude EEG Changes in Preterm Infants at NICU of Al-Zahraa University Hospital

Background: With increase in the incidence of preterm birth, quality of life in premature infants who suffer from perinatal brain injury has become a major concern. Amplitude electroencephalogram has the advantages of being simple bedside monitoring for assessment of brain function and follow up in preterm neonates. Aim of Study: To evaluate the aEEG changes in preterm infants and compare it to cranial ultrasound. Patients and Methods: This was a prospective observational study conducted at the NICU of Al Zahraa University Hospital for a period from May 2020 to May 2021. Our study was conducted on 60 preterm infants (26 - 36 w) in the first 7 days of life with exclusion of obvious congenital anomalies and hypoxic ischemic encephalopathy patients. Cranial ultrasound was performed on all the studied groups then aEEG recording was done for 4 hours. Results: The pattern of aEEG was discontinuous in patients with low gestational age and in infants small for gestational age. The pattern was also discontinuous in infants who had convulsions. Among our studied infants who had PROM, pre-eclampsia and experienced prolonged delivery, some infants had low voltage amplitude recording as well as infants with intraventricular hemorrhage grade III. Conclusion: This study confirms that aEEG background activity is strongly related to gestational age, birth weight, convulsions and IVH. Complications during delivery alter neonatal brain activity and aEEG background. Early aEEG combined with cranial ultrasound increases the sensitivity for detecting abnormal neurological outcome.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning.

Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

Severity Based Light-Weight Encryption Model for Secure Medical Information System

As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

The effect of celastrol in combination with 5-fluorouracil on proliferation and apoptosis of gastric cancer cell lines

Background:Despite the availability of chemotherapy drugs such as 5-fluorouracil(5-FU),the treatment of some cancers such as gastric cancer remains challenging due to drug resistance and side effects.This study aimed to investigate the effect of celastrol in combination with the chemotherapy drug 5-FU on proliferation and induction of apoptosis in human gastric cancer cell lines(AGS and EPG85-257).Materials and Methods:In this in vitro study,AGS and EPG85-257 cells were treated with different concentrations of celastrol,5-FU,and their combination.Cell proliferation was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide(MTT)assay.The synergistic effect of 5-FU and celastrol was studied using Compusyn software.The DNA content at different phases of the cell cycle and apoptosis rate was measured usingflow cytometry.Results:Co-treatment with low concentrations(10%inhibitory concentration(IC10))of celastrol and 5-FU significantly reduced IC50(p<0.05)so that 48 h after treatment,IC50 was calculated at 3.77 and 6.9μM for celastrol,20.7 and 11.6μM for 5-FU,and 5.03 and 4.57μM for their combination for AGS and EPG85-257 cells,respectively.The mean percentage of apoptosis for AGS cells treated with celastrol,5-FU,and their combination was obtained 23.9,41.2,and 61.9,and for EPG85-257 cells 5.65,46.9,and 55.7,respectively.In addition,the 5-FU and celastrol-5-FU combination induced cell cycle arrest in the synthesis phase.Conclusions:Although celastrol could decrease the concentration of 5-fluorouracil that sufficed to suppress gastric cancer cells,additional studies are required to arrive at conclusive evidence on the anticancer effects of celastrol.

Discharging of PCM in Various Shapes of Thermal Energy Storage Systems:A Review

Utilizing the phase change materials in different thermal storage applications attains valuable attention due to the fascinating thermal properties of these materials.The comprehension of the thermal behaviour of phase change materials during the melting and solidification is considered a significant priority in designing the shape of the different containers.In this review,analytical,computational and experimental investigations that address solidification/freezing of phase change materials within thermal energy storage systems are discussed.Emphasis is placed on the role of the shape of adopted containers encompassing planar,spherical,cylindrical and annular vessels.Energy storage for solar thermal applications,waste heat recovery,and thermal management of buildings/computing platforms/photovoltaics has been the topics that benefit from these investigations.For all container shapes,the freezing process is controlled initially by natural convection,and a high solidification rate is observed.Later,the conduction dominates the process,and the freezing rate declines.The temperature and flow of cooling heat transfer fluid affect the solidification process,but the impact of heat transfer fluid temperature is more significant than its flow rate.Also,the freezing time increases with the container’s size and amount of contained PCM.The aspect ratio of the planar and vertical cylindrical cavities substantially influences the discharging time and rate.In contrast,the orientation of the annular cavity has a lower impact on the discharging process.