Search results for: Karthick Dharmalingam

Authors: Karthick Dharmalingam, Stalin Ramakrishnan, Haseena Banu Hedayathullah Khan, Sachidanandanam Thiruvaiyaru Panchanadham, Shanthi Palanivelu

Abstract:

Background: Breast cancer is arising as the most dreadful cancer affecting women worldwide. Hence, there arises a need to search and test for new drugs. Herbal formulations used in Siddha preparations are proved to be effective against various types of cancer. They also offer advantage through synergistic amplification and diminish any possible adverse effects. Tridham (TD) is a herbal formulation prepared in our laboratory consisting of Terminalia chebula, Elaeocarpus ganitrus and Prosopis cineraria in a definite ratio and has been used for the treatment of mammary carcinoma. Objective: To study the restorative effect of Tridham and penta galloyl glucose (a component of TD) on DMBA induced mammary carcinoma in female Sprague Dawley rats. Materials and Methods: Rats were divided into seven groups of six animals each. Group I (Control) received corn oil. Group II– mammary carcinoma was induced by DMBA dissolved in corn oil single dose orally. Group III and Group IV were induced with DMBA and subsequently treated with Tridham and penta galloyl glucose, respectively for 48 days. Group V was treated with DMBA and subsequently with a standard drug, cyclophosphamide. Group VI and Group VII were given Tridham and penta galloyl glucose alone, respectively for 48 days. After the experimental period, the animals were sacrificed by cervical decapitation. The mammary gland tissue was excised and levels of antioxidants were determined by biochemical assay. p53 and PCNA expression were accessed using immunohistochemistry. Nrf-2, Cox-2 and caspase-3 protein expression were studied by Western Blotting analysis. p21, Bcl-2, Bax, Bad and caspase-8 gene expression were studied by RT-PCR. Results: Histopathological studies confirmed induction of mammary carcinoma in DMBA induced rats and treatment with TD and PGG resulted in regression of tumour. The levels of enzymic and non-enzymic antioxidants were decreased in DMBA induced rats when compared to control rats. The levels of cell cycle inhibitory markers and apoptotic markers were decreased in DMBA induced rats when compared to control rats. These parameters were restored to near normal levels on treatment with Tridham and PGG. Conclusion: The results of the present study indicate the antineoplastic effect of Tridham and PGG are exerted through the modulation of antioxidant status and expression of cell cycle regulatory markers as well as apoptotic markers. Acknowledgment: Financial assistance provided in the form of ICMR-SRF by Indian Council of Medical Research (ICMR), India is gratefully acknowledged here.

Keywords: antioxidants, Mammary carcinoma, pentaGalloyl glucose, Tridham

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Authors: G. Hemath Kumar, H. Mohit, K. Karthick

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One of the most important issues in the composite technology is the repairing of parts of aircraft structures which is manufactured from composite materials. In such applications and also for joining various composite parts together, they are fastened together either using adhesives or mechanical fasteners. The tensile strength of these joints was carried out using Universal Testing Machine (UTM). A parametric study was also conducted to compare the performance of the hybrid joint with varying adherent thickness, adhesive thickness and overlap length. The composition of the material is combination of epoxy resin and carbon fibre under the method of reinforcement. To utilize the full potential of composite materials as structural elements, the strength and stress distribution of these joints must be understood. The study of tensile strength in the members involved under various design conditions and various joints were took place.

Keywords: carbon fiber, FRP composite, MMC, automotive

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Authors: Senthil Rajan Dharmalingam, Shamala Nadaraju, Srinivasan Ramamurthy

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Doxorubicin is the most widely used anticancer drugs in chemotherapy treatment. However, problems related to the development of multidrug resistance (MDR) and acute cardiotoxicity have led researchers to investigate alternative forms of administering doxorubicin for cancer therapy. Several methods have been attempted to overcome MDR, including the co-administration of a chemosensitizer inhibiting the efflux caused by ATP binding cassette transporters with anticancer drugs, and the bypass of the efflux mechanism. Co encapsulation of doxorubicin (Dox) and cyclosporine A (CSA) into poly (DL-lactide-co-glycolide) nanoparticles was emulsification-solvent evaporation method using polyvinyl alcohol as emulsion stabilizers. The Dox-CSA loaded nanoparticles were evaluated for particle size, zeta potential and PDI by light scattering analysis and thermal characterizations by differential scanning calorimetry (DSC). Loading efficiency (LE %) and in-vitro dissolution samples were evaluated by developed and validated HPLC method. The optimum particle size obtained is 298.6.8±39.4 nm and polydispersity index (PDI) is 0.098±0.092. Zeta potential is found to be -29.9±4.23. Optimum pH to increase Dox LE% was found 7.1 which gave 42.5% and 58.9% increase of LE% for pH 6.6 and pH 8.6 compared respectively. LE% achieved for Dox is 0.07±0.01 % and CSA is 0.09±0.03%. Increased volume of PVA and weight of PLGA shows increase in size of nanoparticles. DSC thermograms showed shift in the melting peak for the nanoparticles compared to Dox and CSA indicating encapsulation of drugs. In conclusion, these preliminary studies showed the feasibility of PLGA nanoparticles to entrap Dox and CSA and require future in-vivo studies to be performed to establish its potential.

Keywords: doxorubicin, cyclosporine, PLGA, nanoparticles

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Authors: Karthick Gopalan, Selvamohan Thankiah

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Biosurfactants are lipid compounds produced by microbes, which are amphipathic molecules consisting of hydrophophic and hydrophilic domains. In the present investigation, ten bacterial strains were isolated from petroleum oil contaminated sites near petrol bunk. Oil collapsing test, haemolytic activity were used as a criteria for primary isolation of biosurfactant producing bacteria. In this study, all the bacterial strains gave positive results. Among the ten strains, two were observed as good biosurfactant producers, they utilize the diesel as a sole carbon source. Optimization of biosurfactant producing bacteria isolated from petroleum oil contaminated sites was carried out using different parameters such as, temperature (20ºC, 25ºC, 30ºC, 37ºC and 45ºC), pH (5,6,7,8 & 9) and nitrogen sources (ammonium chloride, ammonium carbonate and sodium nitrate). Biosurfactants produced by bacteria were extracted, dried and quantified. As a result of optimization of parameters the suitable values for the production of more amount of biosurfactant by the isolated bacterial species was observed as 30ºC (0.543 gm/lt) in the pH 7 (0.537 gm/lt) with ammonium nitrate (0.431 gm/lt) as sole carbon source.

Keywords: isolation and identification, biosurfactant, microorganism, bioaugmentation

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: Karthick Prasad Gunasekaran, B. Chase Babrich, Saurabh Shirodkar, Hee Hwang

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Construction preparation is crucial for the success of a construction project. By involving project participants early in the construction phase, project managers can plan ahead and resolve issues early, resulting in project success and satisfaction. This study uses quantitative data from construction management projects to determine the relationship between the pre-construction phase, construction schedule, and customer satisfaction. This study examined a total of 65 construction projects and 93 clients per job to (a) identify the relationship between the pre-construction phase and program reduction and (b) the pre-construction phase and customer retention. Based on a quantitative analysis, this study found a negative correlation between pre-construction status and project schedule in 65 construction projects. This finding means that the more preparatory work done on a particular project, the shorter the total construction time. The Net Promoter Score of 93 clients from 65 projects was then used to determine the relationship between construction preparation and client satisfaction. The pre-construction status and the projects were further analyzed, and a positive correlation between them was found. This shows that customers are happier with projects with a higher ready-to-build ratio than projects with less ready-to-build.

Keywords: NLP, BERT, LLM, deep learning, classification

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Authors: Sreekanth T. G., Kishorekumar S., Sowndhariya Kumar J., Karthick R., Shanmugasuriyan S.

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Composites are increasingly being used in industries due to their unique properties, such as high specific stiffness and specific strength, higher fatigue and wear resistances, and higher damage tolerance capability. Composites are prone to failures or damages that are difficult to identify, locate, and characterize due to their complex design features and complicated loading conditions. The lack of understanding of the damage mechanism of the composites leads to the uncertainties in the structural integrity and durability. Delamination is one of the most critical failure mechanisms in laminated composites because it progressively affects the mechanical performance of fiber-reinforced polymer composite structures over time. The identification and severity characterization of delamination in engineering fields such as the aviation industry is critical for both safety and economic concerns. The presence of delamination alters the vibration properties of composites, such as natural frequencies, mode shapes, and so on. In this study, numerical analysis and experimental analysis were performed on delaminated and non-delaminated glass fiber reinforced polymer (GFRP) plate, and the numerical and experimental analysis results were compared, and error percentage has been found out.

Keywords: composites, delamination, natural frequency, mode shapes

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Authors: S. Vadivukkarasi, K. Karthi, M. Karthick, C. Dinesh, S. Santhosh, A. Yogaraj

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The appointment system was designed to minimize patient’s idle time overlooking patients waiting time in hospitals. This is no longer valid in today’s consumer oriented society. Long waiting times for treatment in the outpatient department followed by short consultations has long been a complaint. Nowadays, customers use waiting time as a decisive factor in choosing a service provider. Queuing theory constitutes a very powerful tool because queuing models require relatively little data and are simple and fast to use. Because of this simplicity and speed, modelers can be used to quickly evaluate and compare various alternatives for providing service. The application of queuing models in the analysis of health care systems is increasingly accepted by health care decision makers. Timely access to care is a key component of high-quality health care. However, patient delays are prevalent throughout health care systems, resulting in dissatisfaction and adverse clinical consequences for patients as well as potentially higher costs and wasted capacity for providers. Arguably, the most critical delays for health care are the ones associated with health care emergencies. The allocation of resources can be divided into three general areas: bed management, staff management, and room facility management. Effective and efficient patient flow is indicated by high patient throughput, low patient waiting times, a short length of stay at the hospital and overtime, while simultaneously maintaining adequate staff utilization rates and low patient’s idle times.

Keywords: appointment system, patient scheduling, bed management, queueing calculation, system analysis

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Authors: Venkata Srikanth Meka, Nur Arthirah Binti Ahmad Tarmizi Tan, Muhammad Syahmi Bin Md Nazir, Adinarayana Gorajana, Senthil Rajan Dharmalingam

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Biphasic drug delivery systems are designed to release drug at two different rates, either fast/prolonged or prolonged/fast. A fast/prolonged release system provides a burst drug release at initial stage followed by a slow release over a prolonged period of time and in case of prolonged/fast release system, the release pattern is vice versa. Terminalia catappa gum (TCG) is a natural polymer and was successfully proven as a novel pharmaceutical excipient. The main objective of the present research is to investigate the applicability of natural polymer, Terminalia catappa gum in the design of oral biphasic drug delivery system in the form of mini tablets by using a model drug, buspirone HCl. This investigation aims to produce a biphasic release drug delivery system of buspirone by combining immediate release and prolonged release mini tablets into a capsule. For immediate release mini tablets, a dose of 4.5 mg buspirone was prepared by varying the concentration of superdisintegrant; crospovidone. On the other hand, prolonged release mini tablets were produced by using different concentrations of the natural polymer; TCG with a buspirone dose of 3mg. All mini tablets were characterized for weight variation, hardness, friability, disintegration, content uniformity and dissolution studies. The optimized formulations of immediate and prolonged release mini tablets were finally combined in a capsule and was evaluated for release studies. FTIR and DSC studies were conducted to study the drug-polymer interaction. All formulations of immediate release and prolonged release mini tablets were passed all the in-process quality control tests according to US Pharmacopoeia. The disintegration time of immediate release mini tablets of different formulations was varied from 2-6 min, and maximum drug release was achieved in lesser than 60 min. Whereas prolonged release mini tablets made with TCG have shown good drug retarding properties. Formulations were controlled for about 4-10 hrs with varying concentration of TCG. As the concentration of TCG increased, the drug release retarding property also increased. The optimised mini tablets were packed in capsules and were evaluated for the release mechanism. The capsule dosage form has clearly exhibited the biphasic release of buspirone, indicating that TCG is a suitable natural polymer for this study. FTIR and DSC studies proved that there was no interaction between the drug and polymer. Based on the above positive results, it can be concluded that TCG is a suitable polymer for the biphasic drug delivery systems.

Keywords: Terminalia catappa gum, biphasic release, mini tablets, tablet in capsule, natural polymers

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: R. Stalin, D. Karthick, H. Haseena Banu, T. P. Sachidanandam, P. Shanthi

Abstract:

Background: Breast cancer is emerging as one of the leading cause of cancer related deaths and hence there arises the need to look out for drugs which are more targets specific with minimal side effects. In recent times, there is a shift towards alternative medicine due to low cost and less side effects. Siddha system of medicine is one the oldest system of medicine practiced against various ailments. Tridham (TD) is a herbal formulation prepared in our laboratory consisting of Terminalia chebula, Elaeocarpus ganitrus and Prosopis cineraria in a definite ratio (TD) and its anticancer potential is evaluated in terms of induction of apoptosis. Objective: The present study was designed to investigate the anti proliferative effect of TD and 1,2,3,4,6-penta-O-galloyl-b-D-glucose (PGG), a pure compound isolated from TD on human mammary carcinoma cell line (MCF-7). Materials and Methods: Cell viability was studied using MTT analysis and trypan blue staining. Mitochondrial membrane potential was studied using DAPI staining. The protein and mRNA expressions of pro-apoptotic and anti- apoptotic markers namely Bax, Bad, Bcl-2 and caspases were also assessed by Western Blotting and RT PCR. Results: Viability studies of TD and PGG treated MCF-7 cells showed an inhibition in cell growth in time and dose dependent manner. The alteration in mitochondrial membrane potential was restored through treatment with TD and PGG which was confirmed by DAPI staining. The protein and mRNA expression of pro-apoptotic markers was found to be significantly increased in TD and PGG treated cells with a concomitant decrease in anti-apoptotic markers. Conclusion: The results of the study suggest that TD and PGG exhibit their anticancer effect through its membrane stabilizing property and activation of apoptotic cascade in MCF-7 cells.

Keywords: apoptosis, mammary carcinoma, MCF-7, penta galloyl glucose, Tridham

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Authors: Velvizhi Dharmalingam, Rajalaksmi Ramalingam, Rekha Prabhu, Ilavarasan Raju

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Background: The use of herbal products for various therapeutic regimens has increased tremendously in the developing countries. Elaeocarpus ganitrus(Rudraksha) is a broad-leaved tree, belonging to the family Elaeocarpaceae found in tropical and subtropical areas. It is popular in an indigenous system of medicine like Ayurveda, Siddha, and Unani. According to Ayurvedic medicine, Rudraksha is used in the managing of blood pressure, asthma, mental disorders, diabetes, gynaecological disorders, neurological disorders such as epilepsy and liver diseases. Objectives: The present study aimed to study the physicochemical parameters of Elaeocarpus ganitrus(fruits) and identify the phenolic compounds (gallic acid, ellagic acid, and chebulinic acid). To estimate the microbial load and the antibacterial activity of extract of Elaeocarpus ganitrus for selective pathogens. Methodology: The dried powdered fruit of Elaeocarpus ganitrus was performed the physicochemical parameters (such as Loss on drying, Alcohol soluble extractive, Water soluble extractive, Total ash and Acid insoluble ash) and pH was measured. The dried coarse powdered fruit of Elaeocarpus ganitrus was extracted successively with hexane, chloroform, ethylacetate and aqueous alcohol by cold percolation method. Identification of phenolic compounds (gallic acid, ellagic acid, chebulinic acid) was done by HPTLC method and confirmed by co-TLC using different solvent system.The successive extracts of Elaeocarpus ganitrus and standards (like gallic acid, ellagic acid, and chebulinic acid) was approximately weighed and made up with alcohol. HPTLC (CAMAG) analysis was performed on a TLC over silica gel 60F254 precoated aluminium plate, layer thickness 0.2 mm (E.Merck, Germany) by using ATS4, Visualizer and Scanner with wavelength at 254 nm, 366 nm and derivatized with different reagents. The microbial load such as total bacterial count, total fungal count, Enterobacteria, Escherichia coli, Salmonella species, Staphylococcus aureus and Pseudomonas aeruginosa by serial dilution method and antibacterial activity of was measured by Kirby bauer method for selective pathogens. Results: The physicochemical parameter of Elaeocarpus ganitrus was studied for standardization of crude drug. Among all the successive extracts were identified with phenolic compounds and Elaeocarpus ganitrus extract having potent antibacterial activity against gram-positive and gram-negative bacteria.

Keywords: antimicrobial activity, Elaeocarpus ganitrus, HPTLC, phenolic compounds

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Authors: H. Haseena Banu, D. Karthick, R. Stalin, E. Nandha Kumar, T. P. Sachidanandam, P. Shanthi

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Background of the study: Type 2 diabetes is emerging as the predominant metabolic disorder in the world among adults characterized mainly by the resistance of the insulin sensitive tissues towards insulin followed by the decrease in the insulin secretion. The treatment for this disease usually involves treatment with oral synthetic drugs which are known to cause several side effects. Therefore, identification of new biomarkers as therapeutic target is the need of the hour. miRNAs are small, non–protein-coding RNAs that negatively regulate gene expression by promoting degradation and/or inhibit the translation of target mRNAs and have emerged as biomarkers in predicting diabetes mellitus. Objective of the study: To elucidate the therapeutic role of gallic acid in modulating the alterations in glucose metabolism induced by miRNAs 194 and 135a in Type 2 diabetic rats. Materials and Methods: T2D was induced in rats by feeding them with a high fat diet for 2 weeks followed by intraperitoneal injection of 35 mg/kg/body weight (b.wt.) of streptozotocin. Microarrays were used to assess the expression of miRNAs in control, diabetic and gallic acid treated rats. Gene expression studies were carried out by RT PCR analysis. Results: Forty one miRNAs were differentially expressed in Type 2 diabetic rats. Among these, the expression of miRNA 194 was significantly decreased whereas miRNA 135a was significantly increased in Type 2 diabetic rats. The glucose metabolism was also altered significantly in skeletal muscle of Type 2 diabetic rats. Conclusion: T2D is associated with alterations in the expression of miRNAs in skeletal muscle. Both these miRNAs 194 and 135a play an important role in glucose metabolism in skeletal muscle of diabetic rats. Gallic acid effectively ameliorated the alterations in glucose metabolism. Hence, both these miRNAs can serve as biomarkers and therapeutic targets in diabetes mellitus. The study also establishes the role of gallic acid as therapeutic agent. Acknowledgment: The financial assistance provided in the form of ICMR women scientist by ICMR DHR INDIA is gratefully acknowledged here.

Keywords: gallic acid, high fat diet, type 2 diabetes mellitus, miRNAs

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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