Volume-12 Issue-1, October 2022 – International Journal of Engineering and Advanced Technology (IJEAT)

A38541012122

IJEAT Journal — Fri, 14 Oct 2022 09:21:02 +0000

The International Journal of Engineering and Advanced Technology (IJEAT) has ISSN: 2249-8958 (online), an open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in February, April, June, August, October and December. 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Fighter Aircraft Detection using CNN and Transfer Learning
Motati Dinesh Reddy¹, Sai Venkata Rao Kora², Gnana Samhitha Ch.³
¹Motati Dinesh Reddy, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur (Tamil Nadu), India.
²Sai Venkata Rao Kora, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur (Tamil Nadu), India.
³Gnana Samhitha Ch., Department of Computer Science and Engineering, SRM Institute of Technology, Amaravati (AP), India.
Manuscript received on 30 September 2022 | Revised Manuscript received on 10 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 114-120 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38541012122 | DOI: 10.35940/ijeat.A3854.1012122
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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 work, Deep learning techniques such as Convolutional Neural networks (CNN) and Transfer Learning are used to detect and identify Fighter aircraft or jets. A dataset consisting of 21 different aircraft with 20000 images is being processed using the above algorithms. CNN works on the principle of “pooling,” which progressively reduces the spatial size of the model to decrease the number of parameters and computations in the network. CNN’s are widely used for image detection in different domains, including defense, agriculture, business, face recognition technology, etc. Transfer learning is a machine learning method where a model created for a task is reused as the initial point for a model on a second task. Transfer learning is related to issues such as multi-task learning and concept drift and is not only an area of study in deep learning. The dataset is processed and uses python libraries such as pandas, seaborn, sci-kit- learn, etc., to find any pre-trained patterns and insights. Data is separated into train and test datasets with 80-20 percent of total data, respectively. A model is built using the TensorFlow library for CNN. The metric used is “accuracy.” A transfer learning model is also built to compare the accuracy results and adopt the best-fitting one.
Keywords: Convolution Neural Network, Deep Learning, Artificial Neural Network, Support Vector Machines
Scope of the Article: Deep Learning

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A38531012122

IJEAT Journal — Sat, 08 Oct 2022 12:05:37 +0000

Transformer-Based Abstract Generation of Medical Case Reports
Anusha Verma Chandraju¹, Lydia J Gnanasigamani²
¹Anusha Verma Chandraju, SCOPE, Vellore Institute of Technology, Vellore, India.
²Lydia J Gnanasigamani, SCOPE, Vellore Institute of Technology, Vellore, India.
Manuscript received on 29 September 2022 | Revised Manuscript received on 07 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 110-113 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38531012122 | DOI: 10.35940/ijeat.A3853.1012122
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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: A medical case report gives medical researchers and healthcare providers a thorough account of the symptoms, treatment, and diagnosis of a specific patient. This clinical data is essential because they aid in diagnosing novel or uncommon illnesses, analyzing specific medical occurrences, and enhancing knowledge of current medical education. The summary of the medical case report is needed so that one can decide on further reading as going through the entire contents of a medical case report istime-consuming. In this paper, we present a deep learning methodology for the generation of the automatic summaries of the medical case reports. The final proposed fine-tuned summarizer on the test data set generated a mean precision of 0.4481 and Rouge-1 Score of 0.2803.
Keywords: Transformers, Healthcare, Extractive Summarization, Abstractive Summarization, Medical Research
Scope of the Article: Healthcare Informatics

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A38571012122

IJEAT Journal — Sat, 08 Oct 2022 11:59:55 +0000

An Empirical Survey of Machine Learning-Based Plant Disease Prediction Models
Smita Sankhe¹, Guddi Singh²
¹Smita Sankhe, Ph.D Research Scholar, Department of Computer Science and Engineering, Kalinga University, Naya Raipur (Chhattisgarh), India.
²Dr. Guddi Singh, Faculty, Department of Computer Science and Engineering, Kalinga University, Naya Raipur (Chhattisgarh), India.
Manuscript received on 30 September 2022 | Revised Manuscript received on 07 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 104-109 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38571012122 | DOI: 10.35940/ijeat.A3857.1012122
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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 occurrence of diseases in plants badly impacts the agricultural production, which increases the food insecurity when the diseases are left undetected. Particularly important for ensuring the availability of production of agricultural and food are the major crops, such as maize, rice, and others. Effective control and prevention of diseases in plants are based on disease forecasting and early warning, which is essential for managing and making decisions regarding agricultural productivity. In rural parts of developing nations, observations by knowledgeable providers remain the main method for plant disease identification as of yet. This draws researchers in for ongoing experienced monitoring, which may be cost-prohibitive on large farms. Besides, in some remote areas, farmers require the assistance of the agricultural experts, which is the expensive and timeconsuming process. Hence, automatic disease identification for plants is important to promote the monitoring of large crop fields, which encourages the contribution of the accurate, lessexpensive, automatic, and fast technique to perform the detection of diseases in plants. In this survey, the automatic detection methods used for the plant disease detection based on the deep learning methods are discussed. The importance of the deep learning methods for the detection of disease is demonstrated through the schematic sketch on the other basic machine learning techniques in agricultural applications.
Keywords: Automatic Detection, Plant Diseases, Deep Learning, Agricultural Production, Plant Disease Detection.
Scope of the Article: Deep Learning

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A38561012122

IJEAT Journal — Sat, 08 Oct 2022 11:55:21 +0000

A Review of Plant Disease Prediction Methods for Agricultural Applications
Nirmala Shinde¹, Guddi Singh²
¹Nirmala Shinde, Ph.D Research Scholar, Department of Computer Science and Engineering, Kalinga University, Naya Raipur (Chhattisgarh), India.
²Dr. Guddi Singh, Faculty, Department of Computer Science and Engineering, Kalinga University, Naya Raipur (Chhattisgarh), India.
Manuscript received on 30 September 2022 | Revised Manuscript received on 07 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 98-103 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38561012122 | DOI: 10.35940/ijeat.A3856.1012122
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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: Due to the decrease in plant quality and productivity, plant diseases seem to be responsible for significant economic losses in the world. As a result, farmers nowadays consider plant disease prediction to be an important area of research. To help an accurate prediction of plant disease, numerous techniques have been detailed in the literature. To highlight the many issues with current approaches for problem-solving predictions, we will evaluate various literary works that are focused on plant disease prediction in the agricultural industry. Based on several variables, including different datasets, year of publication and journals, performance metrics, and other considerations, the analyses of various approaches are enhanced in this case, and include the advantages and disadvantages based on the analysis of the methods. Finally, the paper concludes by discussing future research areas and difficulties in improving prediction performance for the plant disease prediction techniques used in the growing agricultural process.
Keywords: Plant Disease Prediction, Agricultural Application
Scope of the Article: Agricultural Informatics and Communication

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A38481012122

IJEAT Journal — Sat, 08 Oct 2022 10:14:37 +0000

Angular Contact Ball Bearing Modeling with Different Types of Coatings
B.T. Loom¹, W.F.H.W. Zamri², A.K. Ariffin³, M.F. Md Din⁴, A. Shamsudeen⁵
¹B.T. Loom, Department of Mechanical & Manufacturing Engineering (JKMP), University Kebangsaan Malaysia, Bangi, 43600, Malaysia.
²W.F.H.W. Zamri, Department of Mechanical & Manufacturing Engineering (JKMP), University Kebangsaan Malaysia, Bangi, 43600, Malaysia.
³A.K. Ariffin, Department of Mechanical & Manufacturing Engineering (JKMP), University Kebangsaan Malaysia, Bangi, 43600, Malaysia.
⁴M.F. Md Din, Faculty of Engineering, National Defence University of Malaysia, Malaysia.
⁵A. Shamsudeen, Department of Mechanical & Manufacturing Engineering (JKMP), University Kebangsaan Malaysia, Bangi, 43600, Malaysia.
Manuscript received on 27 September 2022 | Revised Manuscript received on 03 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 92-97 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38481012122 | DOI: 10.35940/ijeat.A3848.1012122
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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 purpose of this study is to determine the stress distribution of uncoated and coated ball bearings by using finite element analysis. The coatings used in this study are titanium nitride (TiN), titanium carbide (TiC) and chromium nitride (CrN) with a thickness of 5 microns. A contact analysis has been performed on ball bearings to compare the performance between coated and uncoated ball bearings. Boundary loads of 5000 N is used for contact analysis. This study tries to establish a simple, two-dimensional expression for the elastic deformation with the inner ring and ball bearing as a angular or curvature model in terms of the geometry of the coating contact surfaces. The coating of ball and raceway surfaces is a requisite but difficult factor to be determined during design, so it is desirable for engineering to understand the effect of surface coating on the motion of ball and subsurface stresses in bearing. For contact analysis, the maximum contact pressure and maximum stress on the coating, inner ring and ball bearing have been used for comparison. The results of this study show that, among other coatings, TiC provides the best protection for the ball of the ball bearing. This is because the low Poisson’s ratio of TiC in other coated ball bearings helps reduce the stress on the ball bearing, even though TiC has the lowest Young’s modulus in the coating. When a lower boundary load is applied, high COF will also cause an abnormal increase in the maximum stress on the contact surface between the coated or uncoated ball and the inner ring of the ball bearing.
Keywords: Ball Bearings, Coating, Contact Surface, FEM
Scope of the Article: Coating

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A38491012122

IJEAT Journal — Sat, 08 Oct 2022 10:07:22 +0000

Low Earth Orbiting (LEO) Satellite and Orbital Decay
Kaveri Sinha Mahapatra
Kaveri Sinha Mahapatra, North Point Senior Secondary Boarding School, Calcutta (West Bengal), India.

Manuscript received on 27 September 2022 | Revised Manuscript received on 02 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 88-91 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38491012122 | DOI: 10.35940/ijeat.A3849.1012122
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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 article we present a simple but elegant numerical calculation to study the motion of a low earth orbiting (LEO) satellite in gravitational potential and under the influence of quadratic drag force. We solve a set of coupled differential equations to calculate the instantaneous position and velocity of the satellite and calculate the change in radial distance for different aerodynamic coefficients and altitude. We validate our numerical results with theoretical result in the high altitude where the density of air and consequently the drag force is minimum. However the LEO orbits placed in the range of 200 to 400 km faces significant effect of drag force. Satellites at an altitude of 200 km quickly decay and re-entry to earth’s atmosphere occurs. We finally do a short numerical calculation with the parameters of ISS satellite at an altitude of 400 km and compare with the existing results.
Keywords: Aerodynamic Coefficient, ISS Satellite, LEO Satellite, Quadratic Drag,
Scope of the Article: Radar and Satellite

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A38521012122

IJEAT Journal — Sat, 08 Oct 2022 10:00:50 +0000

Alert Clustering using Self-Organizing Maps and K-Means Algorithm
Dayanand D. Ambawade¹, Jagdish W. Bakal²
¹Dayanand Ambawade, Associate Professor, Department of Electronics and Telecommunication Engineering, Sardar Patel Institute of Technology, Mumbai (Maharashtra), India.
²Dr. Jagdish W. Bakal, Professor and Principal, Pillai HOC College of Engineering and Technology, Mumbai (Maharashtra), India.
Manuscript received on 29 September 2022 | Revised Manuscript received on 02 September 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 82-87 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38521012122 | DOI: 10.35940/ijeat.A3852.1012122
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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: Alert correlation is a system that receives alerts from heterogeneous Intrusion Detection Systems and reduces false alerts, detects high-level patterns of attacks, increases the meaning of occurred incidents, predicts the future states of attacks, and detects root cause of attacks. This paper presents self-organizing maps and the k-means machine learning algorithms to reduce the number of alerts by clustering them.
Keywords: Alert, Clustering, Intrusion Detection System, K-means, SOM
Scope of the Article: Clustering

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A38551012122

IJEAT Journal — Sat, 08 Oct 2022 09:54:43 +0000

An Operational Efficiency Assessment of E-Trike and Motorized Tricycles as Public Utility Vehicles
Anna Liza C. Nacion¹, Robin B. Dimla², Dynah D. Soriano³
¹Anna Liza C. Nacion, College of Engineering and Architecture, Department of Industrial Engineering, Don Honorio Ventura State University, Pampanga, Philippines.
²Robin B. Dimla, College of Education, Don Honorio Ventura State University, Pampanga, Philippines.
³Dynah D. Soriano, College of Education, Don Honorio Ventura State University, Pampanga, Philippines.
Manuscript received on 30 September 2022 | Revised Manuscript received on 05 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 76-81 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38551012122 | DOI: 10.35940/ijeat.A3855.1012122
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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 study focused on the operational efficiency of e-trikes and motorized tricycles as public utility vehicles, leading to the analytical comparison of the data gathered. The comparative analysis on both tricycles reveals that e-trikes are more efficient and safer, consume zero fuel, and almost double in passenger capacity when compared to the motorized tricycle. Further, e-trike can enter narrower streets or alley easily for having lesser width. On the other hand, motorized tricycles are faster and can reach higher kilometrage and with a shorter length, it has no problems on approaching slopes and bumps. M/M/1 was used to evaluate the service performance of the electrical and motorized tricycles in the terminal. The results show that the e-trike terminal service rate is more efficient than that of the motorized terminal where commuters spent lesser time waiting to be served. The study recommends the need to understand further the effects, advantages, and disadvantages of both tricycles as regard the planned implementation in the mode of public transportation system. Additionally, further study may be needed focusing on the economic and environmental sustainability, and the use of renewable resources to contribute on the sustainable development and increase operational efficiency of PUVs.
Keywords: E-trike, M/M/1 Queueing System, Motorized Tricycle, Operational Efficiency
Scope of the Article: Operational Research

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A38361012122

IJEAT Journal — Sat, 01 Oct 2022 10:21:53 +0000

Performance Evaluation of Approximate Adders: Case Study
Yamini Devi Ykuntam¹, Bujjibabu Penumutchi², Bala Srinivas Peteti³, Satyanarayana Vella⁴
¹Yamini Devi Ykuntam, Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem (A.P), India.
²Bujjibabu Penumutchi, Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem, (A.P), India.
³Bala Srinivas Peteti, Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem, (A.P), India.
⁴Satyanarayana Vella, Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem (A.P), India.
Manuscript received on 21 September 2022 | Revised Manuscript received on 28 September 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 68-75 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38361012122 | DOI: 10.35940/ijeat.A3836.1012122
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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: A computing device designed to carry out a variety of arithmetic computations. The adder circuit, whose operation must be quick with a small area of occupancy, performs the addition, which is a necessary operation in many other mathematical operations including subtraction, multiplication, and division. There is a mandate for an adder circuit with minimal power consumption, minimal delay, and minimal size in various real-time applications such as processing of signals, pictures & video, VLSI data pathways, processors, neural networks, and many more. There is a new class of adders called approximation adders that operate inaccurately but with favorable area, speed, and power consumption. Since their output is inaccurate, the other names for approximate adders are imprecise adders. This set of adders operates at a high speed thanks to a circuit critical path design that uses fewer components. Additionally, compared to precise adders, the approximate adder circuit has a relatively low component count, resulting in a small footprint and circuits that use less energy. There are different ways to create approximate adders. The area can be predicted by counting the number of circuit components that are present. By examining a number of the critical path’s components, delay can be predicted. Several errors that appear in the output of the particular circuit can be used to calculate the accuracy percentage. This review compares approximate adders from four different categories across the board in terms of design constraints and makes note of the differences between each adder.
Keywords: Approximate Adder, Approximate Mirror Adder (AMA), Approximate Adders using XOR/XNOR gates (AXA), Accuracy, Approximate Adder Designs (APAD).
Scope of the Article: Cloud Computing

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A38421012122

IJEAT Journal — Sat, 01 Oct 2022 10:14:00 +0000

Fuzzy Keyword Search Over Encrypted Data using Cloud Computing
Teena Gupta¹, Rohit K.V.S.S²
¹Teena Gupta, UG Scholar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur (Tamil Nadu), India.
²Rohit K.V.S.S, UG Scholar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur (Tamil Nadu), India.
Manuscript received on 24 September 2022 | Revised Manuscript received on 28 September 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 63-67 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38421012122 | DOI: 10.35940/ijeat.A3842.1012122
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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: Cloud” is a collective term for a large number of developments and possibilities. It is not an invention, but more of a “practical innovation”, combining several earlier inventions into something new and compelling. A cloud computing platform dynamically provisions, configures, reconfigures, and de provisions servers as needed [8]. Servers in the cloud can be physical machines or virtual machines. Security is a critical issue in cloud computing due to the variety of IT services that can be provided through a cloud environment. This paper focuses on the aspect of searching keywords over encrypted data while maintaining integrity of the data. Using a traditional algorithm like AES. Unlike traditional searching algorithms, here we try to implement a fuzzy logic which is based on a NLP technique called N gram. This fuzzy keyword searching significantly increases the efficiency and safety over cloud. This will keep the searching time efficient and acquire great results. The n-gram logic will be used to make sets of keywords which will used in the search implementation. To achieve more accurate results, Jaccard Coefficient will be used to find the similarity between the sets of keywords and rank them based on that. The purpose of this paper is to improve the traditional keyword search over encrypted data using cloud computing using advanced algorithms without compromising over security. Through rigorous security analysis, we show that our proposed solution is secure and maintains the privacy of the file server while efficiently using the fuzzy logic.
Keywords: AES, N-Gram, Cloud Computing, Fuzzy Logic, Jaccard Coefficient
Scope of the Article: Cloud Computing

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