19c96fd51791d8d23b95bf4 20211105034655476 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 11 30 2021 10 4 10.35940/ijrte.10.4 https://www.ijrte.org/download/volume-10-issue-4/ Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India. Dr. M. Sucharitha Dr. P.H.V. Sesha Talpa Sai Department of Mechanical Engineering and Director R & D, Malla Reddy College of Engineering and Technology, Hyderabad, India. Ms. M. L. R. Chaitanya Lahari Associate Professor, Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India Ms. P. Haseena Bee Associate Professor, Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India A pulmonary embolism (PE) occurs when a blood artery in the lungs becomes suddenly blocked, generally owing to a blood clot. PE is a frequent life-threatening illness that should be diagnosed as soon as possible. A novel approach for automatically detecting PE in contrast-enhanced CT images is suggested in this research. To identify PE, computerized tomography (CT) is the main test to capture images. It is quick test, incursive with good quality images, enhanced contrast and multi-sliced images can be obtained. Candidate identification, feature calculation, and classification are all part of the system. The major aims of candidate detection are to include PE with even entire occlusions and to eliminate erroneous diagnosis of tissue and parenchymal disorders. When calculating characteristics, the location and structure of the pulmonary vascular tree, as well as the severity, form, and size of an embolus, are all taken into consideration. The ability of the CAD tool to identify emboli in the sectional and sub sectional pulmonary Arterial Tree (PAT) was examined. 11 30 2021 89 94 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6584.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/D65841110421.pdf Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India. Penumatcha Bharath Varma Dr. Jaypal Medida Professor, Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India, Neeraj Kasheety Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India. Hanumanula Sravya Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India. Chinthapalli Amarnath Reddy Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India Modernization in computers and Machine Learning have created new opportunities for improving the methods involved in trading, Changes have been noticed parallelly at the level of investment decisions, and at the faster executions of trades via algorithms. Nowadays 90% of the trades are placed by algorithms, to execute a transaction, algorithms that follow a trend and construct a set of instructions are used in algorithmic trading. It executes the trades more precisely by precluding the effect of human feelings on trading. It all started way back in the 20th century and nowadays it’s becoming more and more competitive, with more big players entering the market every day. Our research aims to advance the market revolution by developing an Algorithmic Trading approach that will automatically trade user strategies alongside its own algorithms for intraday trading based on different market conditions and user approach, and throughout the day invest and trade with continuous modifications to ensure the best returns for day traders and investors. 11 30 2021 95 100 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6585.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/D65851110421.pdf Computer Science, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India. Roopa Chandrika R Gowri Ganesh N.S Computer Science, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India. Mummoorthy A Computer Science, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India. Gayathri A Computer Science, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India. Uncertainty is prevalent in a wide range of real-world issues. The fuzzy sets, vague sets or intuitionistic fuzzy sets are widely used in recent years for decision making and various analysis where uncertainty is predominant. An extension of fuzzy sets is Hesitant Fuzzy Sets, which deals with ambiguous situations that arise when determining an element's membership degree in a set. Researchers have defined various ideas, extensions, aggregation operators, and measurements to deal with reluctant information as a result of this new approach. Machine leaning algorithms are also exploiting hesitant fuzzy sets for better decision making. 11 30 2021 101 105 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6586.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/D65861110421.pdf Research Scholar, School of Mechanical Engineering, Reva University, Bangalore, India. M.L.R. Chaitanya Lahari P.H.V. Sesha Talpa Sai Professor & Director-R&D, Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India. K.V. Sharma Emeritus Professor, Centre for Energy Studies, Jawaharlal Nehru Technological University, Hyderabad. K.S. Narayanaswamy Professor & Director, School of Mechanical Engineering, Reva University, Bangalore, India. P. Haseena Bee Research Scholar, School of Mechanical Engineering, Reva University, Bangalore, India. S. Devaraj Professor, School of Mechanical Engineering, Reva University, Bangalore, India. The Nusselt number, overall heat transfer, and convective heat transfer coefficients of glycerol-water-based Cu nanofluids flowing in a parallel flow double pipe heat exchanger are estimated using CFD analysis. Single-phase fluid approach technique is used in the analysis. Ansys 19.0 workbench was used to create the heat exchanger model. Heat transfer tests with nanofluids at three flow rates (680<Re<1900) are carried out in a laminar developing flow zone. For testing, a 500 mm long concentric double pipe heat exchanger with tube dimensions of ID=10.2 mm, OD= 12.7 mm, and annulus dimensions of ID=17.0 mm, OD= 19.5 mm is employed. Copper is utilized for the tube and annulus material. This study employed three-particle volume concentrations of 0.2 percent, 0.6 percent, and 1.0 percent. The mass flow rates of hot water in the tube are 0.2, 0.017, and 0.0085 kg/s, while the mass flow rates of nanofluids in the annulus are 0.03, 0.0255, and 0.017 kg/s. The average temperature of nanofluids is 36°C, whereas hot water is 58°C. In comparison to base liquid, the overall heat transfer coefficient and convective HTC of 1.0 percent copper nanofluids at 0.03 kg/s are raised by 26.2 and 46.2 percent, respectively. The experimental findings are compared to CFD values, and they are in close agreement. 11 30 2021 106 110 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.D6587.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/D65871110421.pdf Research Scholar, School of Mechanical Engineering, Reva University, Bangalore, India. M.L.R. Chaitanya Lahari P. Haseena Bee Research Scholar, School of Mechanical Engineering, Reva University, Bangalore, India. P.H.V. Sesha Talpa Sai Professor & Director-R&D, Department of Mechanical Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, India. K.S. Narayanaswamy Professor & Director, School of Mechanical Engineering, Reva University, Bangalore, India. S. Devaraj Professor, School of Mechanical Engineering, Reva University, Bangalore, India. K.V. Sharma Emeritus Professor, Centre for Energy Studies, Jawaharlal Nehru Technological University, Hyderabad, India. Dynamic viscosity of SiO2/22nm nanofluids prepared in a glycerine-water (30:70 by volume) mixture base liquid, referred to as GW70, is measured experimentally. Nanofluids with concentrations of 0.2, 0.6, and 1.0 percent are produced, and viscosity measurements are carried out at temperatures ranging from 20 to 80 oC using a LVDV-2T model Brookfield Viscometer. The particle size and elemental composition of nanoparticles are determined using FESEM and EDX. XRD images confirm the SiO2 peaks in the crystalline structure. The rheology of nanofluids is influenced by the nanoparticle’s concentration. In the experimental temperature and concentration range, nanofluids show Newtonian behavior. The viscosity of nanofluids enhanced as particle concentration increased and reduced as temperature increased. For 1.0 percent vol. concentration at 20oC, the maximum viscosity value is achieved, and for 0.2 percent vol. concentration at 80oC, the lowest viscosity value is observed. The viscosity of the glycerine-water base fluid was also determined at 20, 40, 60, and 80 degrees Celsius. The viscosity ratio of nanofluids to the base liquid is found to be more than one for all the nanofluids. This viscosity data is useful to estimate HTC of glycerine-water-based silica nanofluids. 11 30 2021 111 116 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.C6418.1110421 https://www.ijrte.org/wp-content/uploads/papers/v10i4/C64180910321.pdf