Trends in managing femoral neck fractures with arthroplasty in the ≥ 50 years age group.

Objective: To know about the trends in the management of neck of femur fractures with arthroplasty in patients ≥ 50 years. Methods: It is a retrospective cross-sectional study with data collection from Hospital Management Information System from 1st January 2020 to 31st July 2023. SPSS version 25 was used for data analysis. Mean & standard deviation was reported for quantitative variable & frequency and proportion were reported for qualitative variables. The cross- tabulations were performed to evaluate the association between the variables. Results: Total number of patients in this study was 305. Mean age was 67.80 ± 10.5 SD. Male to female ratio was 150:155. Co-morbidities were found in 126 patients. The surgical options used were Austin Moore prosthesis (64), Cemented Bipolar (36), Hybrid Total Hip Replacement (7), Non-cemented Total Hip Replacement (86), Cemented Total Hip Replacement (32), Uncemented Bipolar (71). Garden Type-2 fracture was noted in 33 patients, Type-3 in 170 patients and Type-4 in 87 patients. Cemented stem was used in 74 patients while 222 patients had non-cemented stem. Conclusion: One quarter of the patients had cemented stem implanted compared to three quarter of the patients who had non-cemented stem.

Sulforaphane and Its Protective Role in Prostate Cancer: A Mechanistic Approach.

The increasing incidence of prostate cancer worldwide has spurred research into novel therapeutics for its treatment and prevention. Sulforaphane, derived from broccoli and other members of the Brassica genus, is a phytochemical shown to have anticancer properties. Numerous studies have shown that sulforaphane prevents the development and progression of prostatic tumors. This review evaluates the most recent published reports on prevention of the progression of prostate cancer by sulforaphane in vitro, in vivo and in clinical settings. A detailed description of the proposed mechanisms of action of sulforaphane on prostatic cells is provided. Furthermore, we discuss the challenges, limitations and future prospects of using sulforaphane as a therapeutic agent in treatment of prostate cancer.

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

Assessing the physico-chemical parameters and some metals of underground water and associated soil in the arid and semiarid regions of Tank District, Khyber Pakhtunkhwa, Pakistan.

Good-quality water and food are the basic needs of humans, plants, and animals. Polluted groundwater and soil directly and indirectly affect organisms, which is the main environmental concern. In the current study, standard protocols of atomic absorption spectrometry were adopted for the investigation of selected metals (lead, chromium, and iron) in the collected groundwater and soil samples. The Pearson correlation coefficient (r) applied to groundwater and soil samples shows a positive perfect correlation among water parameters (conductivity and total dissolved solids) in all three sources. In the hand pump samples between water table (WT) and water source depth (WSD), Pearson correlation coefficient (r) value was found (r = 0.87) while between EC and TDS, it was r = 1. Similarly, in the bore hole samples between WT and WSD (r = 0.66), EC and TDS (r = 1), EC and Cr (r = 0.70), and TDS and Cr (r = 0.70), which showed weaker correlation. In the tube well samples, correlation between EC and TDS was high (r = 1). The correlation coefficient (r) values of the soil parameters in the hand pump (soil) samples between Fe and Cr (r = 0.86), in bore hole samples between Fe and Cr (r = 0.77), in tube well samples between Fe and Cr (r = 0.69), while all the other parameter correlations were found lower (r = 0.60). Between electrical conductivity and total dissolved solids, high relation has been observed between them (r = 1). Overall, results showed that in most of the studied samples, contents of the target metals were found above the allowable limit set by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA).

Heartbeat Sound Signal Classification Using Deep Learning.

Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

Single-Equipment with Multiple-Application for an Automated Robot-Car Control System.

The integration of greater functionalities into vehicles increases the complexity of car-controlling. Many research efforts are dedicated to designing car-controlling systems that allow users to instruct the car just to show it what it should do; however, for non-expert users, controlling the car with a remote or a switch is complicated. So, keeping this in mind, this paper presents an Arduino based car-controlling system that no longer requires manual control of the cars. Two main contributions are presented in this work. Firstly, we show that the car can be controlled with hand-gestures, according to the movement and position of the hand. The hand-gesture system works with an Arduino Nano, accelerometer, and radio-frequency (RF) transmitter. The accelerometer (attached with the hand-glove) senses the acceleration forces that are produced by the hand movement, and it will transfer the data to the Arduino Nano that is placed on hand glove. After receiving the data, Arduino Nano will convert it into different angle values in ranges of 0°â»450° and send the data to the RF receiver of the Arduino Uno, which is placed on the car through the RF transmitter. Secondly, the proposed car system is to be controlled by an android based mobile-application with different modes (e.g., touch buttons mode, voice recognition mode). The mobile-application system is the extension of the hand-gesture system with the addition of Bluetooth module. In this case, whenever the user presses any of the touch buttons in the application, and/or gives voice commands, the corresponding signal is sent to the Arduino Uno. After receiving the signal, Arduino will check this against its predefined instructions for moving forward, backward, left, right, and brake; then it will send the command to the motor module to move the car in the corresponding direction. In addition, an automatic obstacle detection system is introduced to improve the safety measurements to avoid any hazards with the help of sensors placed at the front of the car. The proposed systems are designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (hand-gesture, touch buttons and voice-recognition with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We remark that the proposed systems can be implemented under real conditions at large-scale in the future, which will be useful in automobiles and robotics applications.

Single Intramuscular Methylprednisolone dose in Asthma and Chronic Obstructive Pulmonary Disease Patients on Discharge.

OBJECTIVE: In this pilot study we aimed to evaluate the safety of a single intramuscular methylprednisolone (IM) injection at the time of discharge as a replacement for oral steroid therapy for patients in our population with asthma or chronic obstructive pulmonary disease (COPD). METHODS: This proof-of-concept, open label clinical trial without randomisation was conducted at the Pulmonary Department of Ziauddin Hospital and University, Karachi from January 2018 to March 2018. Patients discharged after in-hospital treatment for exacerbations of either asthma or COPD were recruited for this study. Intramuscular injection of methylprednisolone was administered to these patients who were then followed-up after one week and one month. During that period, information was collected of the patients' self-report of any unscheduled emergency room visit, blood sugar and blood pressure control, symptoms suggestive of thrush, increase gastric acidity and weight gain. For the data analysis, frequency and percentages were calculated with SPSS version 21. RESULTS: A total of 30 patients aged 52.83 ± 14.27 years were recruited for this pilot study. At one month follow-up, no unscheduled emergency room visits were observed in all of the study patients. Symptoms suggestive of oral thrush were recorded in only 2 (6.7%) patients and weight gain was reported by only 5 (16.7%). Controlled blood sugar and blood pressure was reported by all the patients. No incidence of nocturnal symptoms, awakening and dyspepsia were reported. CONCLUSIONS: A single dose of methylprednisolone injection without any obvious side effects over one month among patients with asthma and COPD demonstrated a safe strategy for them.

An Automation System for Controlling Streetlights and Monitoring Objects Using Arduino.

We present an Arduino-based automation system to control the streetlights based on solar rays and object's detection. We aim to design various systems to achieve the desired operations, which no longer require time-consuming manual switching of the streetlights. The proposed work is accomplished by using an Arduino microcontroller, a light dependent resistor (LDR) and infrared-sensors while, two main contributions are presented in this work. Firstly, we show that the streetlights can be controlled based on the night and object's detection. In which the streetlights automatically turn to DIM state at night-time and turn to HIGH state on object's detection, while during day-time the streetlights will remain OFF. Secondly, the proposed automated system is further extended to skip the DIM condition at night time, and streetlights turn ON based on the objects' detection only. In addition, an automatic door system is introduced to improve the safety measurements, and most importantly, a counter is set that will count the number of objects passed through the road. The proposed systems are designed at lab-scale prototype to experimentally validate the efficiency, reliability, and low-cost of the systems. We remark that the proposed systems can be easily tested and implemented under real conditions at large-scale in the near future, that will be useful in the future applications for automation systems and smart homes.

Spatial modeling of rat bites and prediction of rat infestation in Peshawar valley using binomial kriging with logistic regression.

In this study, we propose to develop a geostatistical computational framework to model the distribution of rat bite infestation of epidemic proportion in Peshawar valley, Pakistan. Two species Rattus norvegicus and Rattus rattus are suspected to spread the infestation. The framework combines strengths of maximum entropy algorithm and binomial kriging with logistic regression to spatially model the distribution of infestation and to determine the individual role of environmental predictors in modeling the distribution trends. Our results demonstrate the significance of a number of social and environmental factors in rat infestations such as (I) high human population density; (II) greater dispersal ability of rodents due to the availability of better connectivity routes such as roads, and (III) temperature and precipitation influencing rodent fecundity and life cycle.

Spatial assessment of water quality parameters in Jhelum city (Pakistan).

In this study, we assess the drinking water quality of Jhelum city. Two hundred and ninety-two drinking water samples were randomly collected in the study area. These samples were chemically analyzed for three key toxic (in excess) elements such as pH, total dissolved solids (TDS), and calcium. Geostatistical techniques such as variogram and kriging were used to investigate the spatial variations of these minerals across the city. The spatial structure for each element was found to be anisotropic, and thus, anisotropic variograms were used. The kriging predictions revealed significant concentrations of the above-stated elements at some locations in the study area. While comparing with the World Health Organization, United States Environmental Protection Agency, and Pakistan Environmental Protection Agency standards, the water samples were found to be unsatisfactory for drinking. We conclude that the drinking water in this region is of poor quality and needs proper treatment to make it palatable.

Assessing decision of inpatient or outpatient care in community acquired pneumonia: APT care study.

OBJECTIVE: To estimate the proportion of community-acquired pneumonia patients with disagreement between Confusion, Uraemia, Respiratory rate, Blood pressure, age > 65 years recommendation and physician's decision to hospitalise or not. METHODS: This cross-sectional nation-wide, non-interventional, cross-sectional study was carried out across 10 cities of Pakistan from December 2011 to May 2012, and recruited consenting adult patients with confirmatory diagnosis of community-acquired pneumonia on chest X-ray. Confusion, Uraemia, Respiratory rate, Blood pressure, age > 65 years recommendation for each patient was determined at the time of analysis. This recommendation was compared with treatment decision made by the physician. Disagreement was considered when the physician's decision did not match with the recommendation. SPSS 18 was used for data analysis. RESULTS: Of the 352 patients, 201(57.10%) were males. The overall mean age was 50.67±18.45 years. In 140(39.77%) patients there was disagreement between Confusion, Uraemia, Respiratory rate, Blood pressure, age > 65 years recommendation and physician's decision regarding hospitalisation or outpatient care. Of the 352 cases 132(37.50%) were hospitalised despite the recommendation of outpatient treatment. CONCLUSIONS: In almost four out of every 10 patients there was disagreement between Confusion, Uraemia, Respiratory rate, Blood pressure, age > 65 years recommendation and the physician's decision regarding hospitalisation of community-acquired pneumonia patients.

Thermal emissivity of avian eggshells.

The hypothesis has been tested that evolution has resulted in lower thermal emissivity of eggs of birds breeding openly in cold climates than of eggs of birds that nest under protective covering or in warmer climates. Directional thermal emissivity has been estimated from directional-hemispherical reflectance spectra. Due to several methodological difficulties the absolute emissivity is not accurately determined, but differences between species are obvious. Most notably, small waders of the genus Calidris, breeding in cold climates on the tundra, and in most cases with uniparental nest attendance, have low directional emissivity of their eggshells, about 0.92 when integration is carried out for wavelengths up to 16µm. Species belonging to Galloanserinae have the highest directional emissivity, about 0.96, of their eggs. No differences due to climate or breeding conditions were found within this group. Eggs of most other birds tested possess intermediate emissivity, but the values for Pica pica and Corvus corone cornix are as low as for Calidris. Large species-dependent differences in spectral reflectance were found at specific wavelengths. For instance, at 4.259µm the directional-hemispherical reflectance for galliforms range from 0.05 to 0.09, while for Fratercula arctica and Fulmarus glacialis it is about 0.3. The reflection peaks at 6.5 and 11.3µm due to calcite are differentially attenuated in different species. In conclusion, the hypothesis that evolution has resulted in lower thermal emissivity of bird eggs being exposed in cold climates is not supported by our results. The emissivity is not clearly related to nesting habits or climate, and it is unlikely that the small differences observed are ecologically important. The spectral differences between eggs that nevertheless exist should be taken into account when using infrared thermometers for estimating the surface temperature of avian eggs.

Green biocides, a promising technology: current and future applications to industry and industrial processes.

The study of biofilms has skyrocketed in recent years due to increased awareness of the pervasiveness and impact of biofilms. It costs the USA literally billions of dollars every year in energy losses, equipment damage, product contamination and medical infections. But biofilms also offer huge potential for cleaning up hazardous waste sites, filtering municipal and industrial water and wastewater, and forming biobarriers to protect soil and groundwater from contamination. The complexity of biofilm activity and behavior requires research contributions from many disciplines such as biochemistry, engineering, mathematics and microbiology. The aim of this review is to provide a comprehensive analysis of emerging novel antimicrobial techniques, including those using myriad organic and inorganic products as well as genetic engineering techniques, the use of coordination complex molecules, composite materials and antimicrobial peptides and the use of lasers as such or their modified use in combination treatments. This review also addresses advanced and recent modifications, including methodological changes, and biocide efficacy enhancing strategies. This review will provide future planners of biofilm control technologies with a broad understanding and perspective on the use of biocides in the field of green developments for a sustainable future.

Analysis on congestion mechanism of CAVs around traffic accident zones.

Unexpected traffic accidents cause traffic congestion and aggravate the unsafe situation on the roadways. Reducing the impact of such congestion by introducing Connected and Autonomous Vehicles (CAVs) into the traditional traffic flow is possible. It requires estimating the incident's duration and analyzing the incident's impact area to determine the appropriate strategy. To guide the driver in making efficient and accurate judgments and avoiding secondary traffic congestion, the Cooperative Adaptive Cruise Control (CACC) model with dynamic safety distance and the Intelligent Driver Model (IDM) based on the safety potential field theory are introduced to build the evolution model of accidental traffic congestion under diversion interference and non-interference. The Huatao Interchange section of the Inner Ring Highway in the Banan District of Chongqing, China, was selected as the test section for simulating mixed traffic flow under different CAVs permeability (Pc). The relationship between the evacuation time, evacuation traffic volume, and the accident impact degree index (including the farthest queue length and accident duration) under the diversion intervention scenario was analyzed, respectively. The results of the study indicate that the higher the penetration of CAVs, the more significant the improvement in traffic flow occupancy, flow, and speed. Diversion interventions reduce congestion, about 50 % of the duration without interventions, when Pc ≤ 80 %. The traffic volume of diversion interference is non-linearly positively correlated with the maximum queue length, and the earlier the interference time, the stronger the positive correlation. The negative correlation between the interference time and queue length is weak at low evacuation traffic volume. With the increase in evacuation traffic volume, the influence of evacuation time on queue length becomes stronger. The maximum queue length value interval under different conditions is [348 m, 3140 m], and the shortest evacuation time is [1649 s, 2834 s]. The traffic flow data obtained from the simulation are imported into the episodic traffic congestion evolution model. The congestion evaluation indexes are calculated under non-interference and interference measures and compared with the simulation results. The maximum relative error is within 5.38 %. The results can be of great significance in relieving congestion caused by traffic accidents and promptly restoring road capacity.

A novel methodology for stabilization of silver nanoparticles on cotton, nylon and cotton/nylon fabrics using chitosan and triethyl orthoformate for enhanced and elongated antibacterial performance.

In the present study, the commercially available three different fabrics cotton, nylon and cotton/nylon were modified by chitosan and silver nanoparticles using a crosslinker triethyl orthoformate (TEOF). Resulted cotton­silver (Ag-Cs-Cot), nylon­silver (Ag-Cs-Nyl) and cotton-nylon silver (Ag-Cs-Cot-Nyl) fabrics showed significant anti-bacterial activity even after 50 washing cycles. Silver nanoparticles were prepared by reducing silver nitrate through sodium borohydride at 0 °C. In FTIR spectra the peak at near 1650 cm-1 confirmed that TEOF mediated attachment of chitosan with fabrics (due to C=N) and the stretching of secondary amine near the 3375 cm-1 indicated the silver attachment to the amine group of the chitosan. In Scanning Electron Microscope (SEM) images smooth surfaces of fabrics without any damage by modification process were observed. The antibacterial activity was Analyzed by agar diffusion and broth dilution assays against Escherichia coli and Staphylococcus aureus bacterial strains and results showed 90% bacterial inhibition against E. coli and 89% bacterial inhibition against S. aureus. For testing the antibacterial durability, the modified fabrics were washed with non-ionic detergent (10g/l) for 15 minutes under aggressive stirring (100 rpm) at room temperature. The modified fabrics retained antibacterial activity over the 50 washing cycles. Finally, the commercial potential of cotton-silver fabric was evaluated by stitching it with the socks of football players and interestingly results showed that the modified fabric on the socks showed more than 90% bacterial inhibition as compared to the plain fabric after 70 minutes of playing activity.

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach.

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, etc., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself. Self-citation is important to evaluate an author's research profile and has gained popularity recently. Although different criteria are found in the literature regarding appropriate self-citation, self-citation does have a huge impact on a researcher's scientific profile. This study carries out two cases in this regard. In case 1, the qualitative aspect of the author's profile is analyzed using hand-crafted feature engineering techniques. The sentiments conveyed through citations are integral in assessing research quality, as they can signify appreciation, critique, or serve as a foundation for further research. Analyzing sentiments within in-text citations remains a formidable challenge, even with the utilization of automated sentiment annotations. For this purpose, this study employs machine learning models using term frequency (TF) and term frequency-inverse document frequency (TF-IDF). Random forest using TF with Synthetic Minority Oversampling Technique (SMOTE) achieved a 0.9727 score of accuracy. Case 2 deals with quantitative analysis and investigates direct and indirect self-citation. In this study, the top 2% of researchers in 2020 is considered as a baseline. For this purpose, the data of the top 25 Pakistani researchers are manually retrieved from this dataset, in addition to the citation information from the Web of Science (WoS). The self-citation is estimated using the proposed model and results are compared with those obtained from WoS. Experimental results show a substantial difference between the two, as the ratio of self-citation from the proposed approach is higher than WoS. It is observed that the citations from the WoS for authors are overstated. For a comprehensive evaluation of the researcher's profile, both direct and indirect self-citation must be included.

Performance evaluation of machine learning models on large dataset of android applications reviews.

With an ever-increasing number of mobile users, the development of mobile applications (apps) has become a potential market during the past decade. Billions of users download mobile apps for divergent use from Google Play Store, fulfill tasks and leave comments about their experience. Such reviews are replete with a variety of feedback that serves as a guide for the improvement of existing apps and intuition for novel mobile apps. However, application reviews are challenging and very broad to approach. Such reviews, when segregated into different classes guide the user in the selection of suitable apps. This study proposes a framework for analyzing the sentiment of reviews for apps of eight different categories like shopping, sports, casual, etc. A large dataset is scrapped comprising 251661 user reviews with the help of 'Regular Expression' and 'Beautiful Soup'. The framework follows the use of different machine learning models along with the term frequency-inverse document frequency (TF-IDF) for feature extraction. Extensive experiments are performed using preprocessing steps, as well as, the stats feature of app reviews to evaluate the performance of the models. Results indicate that combining the stats feature with TF-IDF shows better performance and the support vector machine obtains the highest accuracy. Experimental results can potentially be used by other researchers to select appropriate models for the analysis of app reviews. In addition, the provided dataset is large, diverse, and balanced with eight categories and 59 app reviews and provides the opportunity to analyze reviews using state-of-the-art approaches.

Nano-Selenium Antagonized Cadmium-Induced Liver Fibrosis in Chicken.

Cadmium is a global ecological toxic pollutant; in animals, hepatotoxic fibrosis is caused by bioaccumulation of Cd through food chains. We determined the path of nano-Se antagonism in Cd-induced hepatocyte pyroptosis by targeting the APJ-AMPK-PGC1α pathway, using an in vivo model of hepatotoxicity. All 1-day-old chicks were treated with Cd (140 mg/kg BW/day) and/or nano-Se (0.3 or 0.6 mg/kg BW/day) for 90 days. The result showed that Cd (1.55 ± 0.148) activated NLRP3 inflammasome 49.903% as compared to the Con group (1.034 ± 0.008) to release the inflammasome as a result of hepatocyte pyroptosis (2.824 ± 0.057). Compared with the Con group (1.010 ± 0.021), Kupffer cells were 219.109% more to activate astrocytes through the APJ-AMPK-PGC1α pathway, resulting in 185.149% more hepatic fibrosis. However, the fibrosis degree of the H-Se + Cd group (1.252 ± 0.056) was 56.5278% (p < 0.001) lower than that of the Cd group (2.880 ± 0.124). Therefore, this study established that pyroptotic hepatocytes and Kupffer cells could be targeted for nano-Se antagonizing Cd toxicity, which reveals a potential new approach targeting astrocytes for the treatment of liver fibrosis triggered by Cd pollution.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 µm) to discriminate vegetation species.

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images.

COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification.