Volume-11 Issue-2, July 2022 – International Journal of Recent Technology and Engineering (IJRTE)

B71700711222

IJRTE Journal — Sat, 06 Aug 2022 08:50:16 +0000

Influence of Colloidal Silica and Partial Replacement of Cement with Sugarcane Bagasse Ash and Glass Fiber in Concrete
Shaikh Mohd Zubair¹, P. R. Makheja²
¹P.R. Makheja, P.G. Student, Department of Civil Engineering, Deogiri Institute of Engineering and Management Studies, Aurangabad (Maharashtra), India.
²Shaikh Mohd Zubair, Assistant Professor, Department of Civil Engineering, Deogiri Institute of Engineering and Management Studies, Aurangabad (Maharashtra), India.
Manuscript received on 29 June 2022 | Revised Manuscript received on 02 August 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022. | PP: 177-182 | Volume-11 Issue-2, July 2022. | Retrieval Number: 100.1/ijrte.B71700711222 | DOI: 10.35940/ijrte.B7170.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In today’s world, many researchers use either industrial or agricultural waste product of invaluable value as a source of cementinous material in the construction industry. Waste reduction is a priority when recycling industrial and agricultural waste from society. Sugarcane bagasse (SCBA) is a fibrous waste from the sugar industry. In this experimental study, bagasse ash has been physically and chemically characterized and partially replaced with cement, which is OPC, in proportions of 7% and 14% by weight, and thereafter it is added in mixture to make concrete. Fresh concrete tests such as slump cone test and hardened concrete test such as compressive strength, split tensile strength and flexural strength were carried out at the age of 7 and 28 days. The result obtained shows the mechanical properties of the concrete for different percentages of bagasse ash, glass fiber along with effect of colloidal silica in concrete.
Keywords: Bagasse ash, Colloidal silica, Compressive strength, Flexural strength, Split tensile strength.
Scope of the Article: Concrete Engineering

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B71890711222

IJRTE Journal — Sat, 30 Jul 2022 09:25:51 +0000

Applying and Improving Accuracy of Heart Disease Prediction Model using Meta-classifiers and Ensemble Learning Methods with Feature Selection
Uma K¹, M Hanumanthappa²
¹Uma K, Research Scholar, Department of Computer Science and Applications, Bangalore University, Bangalore (Karnataka), India.
²Dr. M Hanumanthappa, Professor, Department of Computer Science and Applications, Bangalore University, Bangalore (Karnataka), India.
Manuscript received on 30 June 2022 | Revised Manuscript received on 17 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 172-176 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71890711222 | DOI: 10.35940/ijrte.B7189.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Healthcare industry is a significant sector for producing an enormous amount of data daily. The lack of helpful information is the primary motive for introducing machine learning or data mining techniques for extracting the required pattern needed to make a decision. Globally, heart disease is the leading cause of death. Prediction of heart disease early may help the survival of the patient life. This paper explores the machine learning technologies, ensemble learning, and meta-classifier to predict heart disease with feature selection methods to improve the accuracy. It presents a performance comparison between classifiers, ensemble learning methods, and meta-classifier.
Keywords: Machine Learning, Ensemble Method, Heart Disease, Meta-classifier, Feature Selection Methods.
Scope of the Article: Machine Learning

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B71760711222

IJRTE Journal — Sat, 16 Jul 2022 11:18:51 +0000

Mobile Robot for Automated Storage & Retrieval System
Suyash Zagade¹, B.K. Patle²
¹Suyash Zagade, Student, Department of Mechanical, MIT School of Engineering, MIT ADT University, Loni Kalbhor, Pune (Maharashtra), India.
²Prof. Dr. B.K. Patle, Professor, Department of Mechanical, MIT School of Engineering, MIT ADT University, Loni Kalbhor, Pune (Maharashtra), India.
Manuscript received on 30 June 2022 | Revised Manuscript received on 14 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 165-171 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71760711222 | DOI: 10.35940/ijrte.B7176.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The automated storage and retrieval systems (ASRS) are major material handling support systems that are commonly used in the automated factories, distribution centers, warehousing, and non-manufacturing environments. These robot-based handling systems are increasingly applied in distribution centers and are of high demand in e-commerce operations because of their less space requirement, higher operational flexibility and continuous ability to work thereby continuously produce output 24/7. The study will be focused on the advantages and applications of this robotic system in the Automated Storage & Retrieval System, thereby comparing it with the robot operating on the ground.
Keywords: ASRS, Robotic Handling System, E-Commerce Operations, Vertical and Horizontal Tracks
Scope of the Article: Robotic

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B71820711222

IJRTE Journal — Fri, 08 Jul 2022 12:09:46 +0000

Intelligent Deployment Strategy for Heterogeneous Nodes to Increase the Network Lifetime of Wireless Sensor Networks
Sidhartha Sankar Dora¹, Prasanta Kumar Swain²
¹Sidhartha Sankar Dora, Ph.D. (Pursuing), Sriram Chandra Bhanja Deo University, Baripada (Odisha), India.
²Dr. P.K.Swain, Assistant Professor, Department of Computer Application, North Orissa University, India.
Manuscript received on 27 June 2022 | Revised Manuscript received on 02 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 159-164 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71820711222 | DOI: 10.35940/ijrte.B7182.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The applications of wireless sensor networks are increased day by day for different applications. Heterogeneous wireless sensor networks offer limitless possibilities because to their expandable capabilities, such as diverse computing power and sensing range, but they also represent significant issues due to the scarcity of energy, which is typically non-renewable. The node deployment, coverage area, connectivity, power depletion and network life time are major issues in wireless sensor networks. We have deployed heterogeneous sensor nodes on the basis of energy to design a heterogeneous network model and applied intelligent node deployment techniques by using metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to minimize the power depletion of sensor nodes for enhancing network life time using multi-hop transmission in this paper. The benefits of heterogeneity have been revealed by our experimental findings.
Keywords: Index Terms: Wireless Sensor Networks, Genetic Algorithm, Particle Swarm Optimization, Power Depletion, Network Lifetime
Scope of the Article: Wireless Sensor Networks

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B71810711222

IJRTE Journal — Fri, 08 Jul 2022 12:05:31 +0000

Effective Preprocessing of Medical Images using Denoising Techniques
S. Asha¹, M. Parvathy²
¹S. Asha, Research Scholar, Department of Computer Science and Engineering, Sethu Institute of Technology, Pulloor, Kariapatti (Tamil Nadu), India.
²Dr. M. Parvathy, Professor and Head, Department of Computer Science and Engineering, Sethu Institute of Technology, Pulloor, Kariapatti (Tamil Nadu), India.
Manuscript received on 30 June 2022 | Revised Manuscript received on 05 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 153-158 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71810711222 | DOI: 10.35940/ijrte.B7181.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Since the last few decades, image denoising is one of the most widely concentrated areas of research in the domain of image processing. A wide variety of denoising algorithms have been explored to date, but the problem of noise prevention in Magnetic Resonance Images is still a great barrier to the diagnosis and treatment of certain diseases. This paper mainly focuses on the study and analysis of different Denoising algorithms, the type of noise handled, and their efficiency. Preprocessing of medical images is considered one of the important steps that can enhance the accuracy in the prediction of various diseases. The presence of noise and other artifacts are believed to degrade the prediction accuracy which is the important metric that directs physicians to prolong further in providing clinical guidance to the patients. Most of the algorithms perform denoising in the complex domain. Deep learning-based Denoising algorithms are found to produce more promising results than traditional ones. However, the number of training samples and the training time are some limitations worth mentioning. Magnetic Resonance Images are sources of input for medical diagnosis of a variety of diseases. On removal of noise, these images can go a long way in the early diagnosis of numerous fatal diseases and can save lives. The predominant objective of this summary is to direct the researchers to choose prompt denoising techniques appropriate for their applications despite the available limitations in the same. This review is comprehended with the main aim of suggesting effective image denoising approaches that can go a long way in enhancing the quality of biomedical images.
Keywords: Deep Learning, Denoising, Image Processing, Magnetic Resonance Imaging, preprocessing.
Scope of the Article: Deep Learning

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B71800711222

IJRTE Journal — Fri, 08 Jul 2022 12:00:55 +0000

Economic Load Dispatch by Improved Drone Optimization Technique
Manish Kashyap¹, Achala Jain², Vinita Swarnakar³
¹Manish Kashyap, Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai (Chhattisgarh), India.
²Dr. Achala Jain, Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai (Chhattisgarh), India.
³Vinita Swarnakar, Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai (Chhattisgarh), India.
Manuscript received on 30 June 2022 | Revised Manuscript received on 05 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 149-152 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71800711222 | DOI: 10.35940/ijrte.B7180.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper, the ELD problem is resolved via ABC (Artificial BEE Colony) technique. The major goal of this study is to use the IDO method to present very efficient & reliable approach for solving ED problem in Power system. The suggested approach is used to solve a variety of non-convex ED issues, including banned operating zones with ramp rate constraints. This problem is described as an optimization of the objective function and minimization of the overall operating cost while gratifying all allied constraints, accompanied by the lowest down & up time limitations, startup cost, and spinning reserve. A six generators scheduling problem is discussed, along with its formulation, representation, and simulation result.
Keywords: Artificial BEE Colony (ABC), Improved Drone Optimization (IDO), Economic Load Dispatch/ Economic Dispatch (ED), Unit Commitment (UC), Artificial Intelligence (AI).
Scope of the Article: Artificial Intelligence

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B71780711222

IJRTE Journal — Fri, 08 Jul 2022 11:55:53 +0000

Design and Development of Inspection Test Rig with Stamping
Sneha Magar¹, Mangesh Dhavalikar²
¹Sneha Magar, Software Engineering Intern, Dassault Systemes Solutions Lab Private Ltd., Mumbai (Maharashtra), India.
²Prof. Mangesh Dhavalikar, Principal Investigator, MIT Art, Design & Technology University, Loni Kalbhor (Maharashtra), India.
Manuscript received on 29 June 2022 | Revised Manuscript received on 06 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 144-148 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71780711222 | DOI: 10.35940/ijrte.B7178.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Stamping and inspection are tasks that have been done manually since the beginning of time. Doing these tasks manually is time consuming, there is a possibility of error in it and it gets hectic after a while so it cannot be performed continuously. Automation has been simplifying and reducing the possibility of error over the recent years. In this project we will be designing and developing a prototype for stamping and inspection of metal cans using pneumatics and programmable logic controller (PLC). The stamping is done using the mechanism of quick retrieval by pressurized air. We aim to inspect cans of different sizes and make the system compatible to a wider range of industries. Different sizes of cans will require different pressure while stamping which will be programmed and stamping will be done accordingly with the required pressure. We can further develop the system to automate the whole production including pouring of material in cans, lidding, packaging, stamping and sorting. This work will propose a 3D design on the electropneumatic system and all the components used will also be discussed. This article is unique due to the concept of using one conveyor belt for two different sizes of cans. With the help of this design we can design different systems which can be compatible for multiple products in different industries. This might turn out to be a great solution for small scale industries that want to automate their plants but have not been able to due to high initial cost.
Keywords: Automation, Inspection, Pneumatics, Stamping
Scope of the Article: Software Test Automation and Tools

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B71750711222

IJRTE Journal — Fri, 08 Jul 2022 11:50:56 +0000

Detection of Diabetic Retinopathy using Deep Learning: A Review
Amnaya Pradhan¹, Neha Sharma²
¹Amnaya Pradhan, Department of Computer Science Engineering, S.R.M. Institute of Science and Technology, Chennai (Tamil Nadu), India.
²Neha Sharma, Department of Computer Application, Panjab University, (Chandigarh), India.
Manuscript received on 29 June 2022 | Revised Manuscript received on 05 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 138-143 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71750711222 | DOI: 10.35940/ijrte.B7175.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Throughout the globe, 1.6 million people annually fall prey to dia- betes. And an alarming total of 422 million people throughout the world have been diagnosed with diabetes, most of the contribution to this number being from low and middleincome countries. Diabetic retinopathy is the number one cause of blindness in the world. It generally affects people from ages 25 to 65. It occurs when the blood vessels present in the retina get damaged by hyper – glycemia or prevents blood from passing through the eyes. It is crucial to treat diabetic retinopathy early. If left untreated, it eventually leads to blindness. The proposed methodology is to use Convolutional Neural Networks with ResNet in order to diagnose diabetic retinopathy. Fundal cameras are used to obtain retinal images. The aim is to detect and prevent this disease, where it is challenging to perform medical tests. As per the research study, the images will be prepro- cessed, segmented, enhanced, and then the extraction of features such as micro aneurysms and hemorrhages will occur. Based on this, the disease will be clas- sified into mild, moderate, severe, or proliferative. In the future, this model may also be used to detect other conditions, such as glaucoma and macular degener- ation.
Keywords: ResNet, Diabetes, Convolutional Neural Networks, Micro Aneurysms, Hemorrhages, Retinal Images.
Scope of the Article: Deep Learning

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B71600711222

IJRTE Journal — Fri, 08 Jul 2022 11:45:13 +0000

An Analysis of Methods for Forecasting Epidemic Disease Outbreaks using Information from Social Media
Disha Sushant Wankhede¹, Rohan Rajendra Sadawarte², Mahek Ibrahim Mulla³, Shreya Rahul Jadhav⁴
¹Mrs. Disha Sushant Wankhede, Assistant Professor, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India.
²Rohan Rajendra Sadawarte, Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
³Mahek Ibrahim Mulla, Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
⁴Shreya Rahul Jadhav, Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
Manuscript received on 27 June 2022 | Revised Manuscript received on 02 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 128-137 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71600711222 | DOI: 10.35940/ijrte.B7160.0711222
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Abstract: Predicting the rise or fall of an epidemic or pandemic is an essential part of establishing control over it. Post-World War 1, when there was an outbreak of the “Black Plague” there weren’t any means to analyze and predict. Although today we are equipped with tools like Machine Learning and Artificial Intelligence which have certainly enabled us to prevent unnecessary loss of life. It helps prepare the health officials to build the infrastructure and interpret the intensity of preparedness regulation of resources. The aim of this survey is to analyze and shed some light on the various algorithms and methods such as – regression models, neural networks, ARIMA, etc. Before building any model, gathering and processing the data is also essential. Hence our paper also focuses on which social media platforms proved beneficial in comparison to all we found and then made fit to be incorporated into the models. While researching for this paper, we observed that every disease has a different transmission type that leads to an outbreak and is a key factor in constructing a model. The literature evaluation in this work is centered on various prediction algorithms and their strategies for extracting online data from social media sites like Facebook and Twitter, all of which have drawn a lot of interest in early disease diagnosis for public health.
Keywords: Machine Learning, Artificial Intelligence, social media, Pandemic, Epidemic, Outbreak, Covid-19, Influenza, Regression Models, Neural Networks.
Scope of the Article: Machine Learning

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B71510711222

IJRTE Journal — Fri, 08 Jul 2022 11:39:29 +0000

User Interface and AI Navigation System for Autonomous Vehicle
Aalhad Satav¹, Nishigandha Patel²
¹Aalhad Satav*, M.Tech Student, Department of Mechanical Engineering, MIT ADT University, Pune (Maharashtra), India.
²Nishigandha Patel, Assistant Professor, Department of Mechanical Engineering, MIT ADT University, Pune (Maharashtra), India.
Manuscript received on 26 June 2022 | Revised Manuscript received on 02 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 122-127 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71510711222 | DOI: 10.35940/ijrte.B7151.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The issue of vehicle accidents due to human error and behavior (rash driving, drink and drive, etc.) is on rising resulting into fatality and economical loss. In order to overcome these issues, the autonomous vehicle is being explored as a probable solution. Driver will be replaced by AI based autonomous system; The main objective is to convert the manual operated EV into autonomous vehicle. The project work is divided into three main parts i.e., Control System for Vehicle Control, AI (Artificial Intelligence) and UI (User Interface). The main objective of User Interface is to give information regarding vehicles health i.e. battery remaining, charging time, distance to be covered, etc. AI will be use as decision making system or we can say brain of the autonomous vehicle. System Control or Vehicle Control System is to control the vehicle’s steering, acceleration and braking system with the help of PID controller while the vehicle is being operated in autonomous mode. The main purpose is to make an autonomous EV for the passengers to go to their selected destination with a single click by selecting destination on display (UI). So the vehicle control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn, during acceleration the factors that are considered are; at what speed the vehicle needs to move in an straight path or during a turn and the braking system is operated with the help of LIDAR where if it detects an object at what distance it needs to apply the brakes and when to release the brakes. All the factors and conditions required in developing the autonomous vehicle will be considered, as use cases to improve the accuracy of autonomous vehicle, resulting into achieving desired objective of safe travel.
Keywords: EV, UI, LIDAR, Autonomous Vehicle, AI, Vehicle control
Scope of the Article: Artificial Intelligence

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