Volume-11 Issue-6, May 2022 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

F95610512623

IJITEE Journal — Sat, 13 May 2023 10:22:24 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. 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Machine Learning Based Product Comparison for E-Commerce Websites
Mahalakshmi K¹, Keerthika R, Sruthi R²
¹Mahalakshmi K, Research Supervisor, Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.
²Prof. Keerthika R, Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.
³Sruthi R, Student, Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 06 April 2023 | Revised Manuscript received on 17 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 38-43 | Volume-12 Issue-6, May 2023 | Retrieval Number: 100.1/ijitee.F95610512623 | DOI: 10.35940/ijitee.F9561.0512623

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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: Online shopping through e-commerce has gained widespread popularity among consumers, revolutionizing the operations of businesses in the global market. This paper examines the benefits of e-commerce, including its convenience and ease of comparing prices and products, as well as the challenges customers may face when selecting the optimal product. To overcome this difficulty, the paper proposes a real-time online consumer behaviour prediction system that anticipates a visitor’s purchasing intent using session and visitor data. It assesses the effectiveness of continuous learning using the Naive Bayes strategy. The article also focuses on developing a recommendation system that strikes a balance between increasing precision and safeguarding users’ privacy, utilizing the Prize dataset to assess the system’s accuracy. Additionally, the paper delves into the domain of opinion mining, outlining its objectives and responsibilities, including anticipating sentiment, summarising aspect-based sentiment, and predicting the helpfulness of online feedback and reviews.

Keywords: CLNB, Machine learning, Deep learning, Price Data, Quality purchases, Web mining, Recommendation.
Scope of the Article: Machine learning

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F99160511622

IJITEE Journal — Mon, 23 May 2022 09:22:52 +0000

Machine Learning Approach for Big-Mart Sales Prediction Framework
Sanjay. N Gunjal¹, D.B Kshirsagar², B.J Dange³, H.E Khodke⁴, C.S Kulka⁵
¹Dr. Gunjal Sanjay Nana*, Assistant Professor, Department of Computer Engineering, Sanjivani COE, Kopargaon Savitribai Phule Pune University, Pune (Maharashtra), India.
²Dr. D.B Kshirsagar, HOD and Professor, Department of Computer Engineering, Sanjivani COE, Kopargaon, Savitribai Phule Pune University, Pune (Maharashtra), India.
³Dr. B.J Dange, Associate Professor, Department of Computer Engineering, Sanjivani COE, Kopargaon, Savitribai Phule Pune University, Pune (Maharashtra), India.
⁴Dr. H.E Khodke, Assistant Professor, Department of Computer Engineering Dept, Sanjivani COE, Kopargaon, Savitribai Phule Pune University, Pune (Maharashtra), India.
⁵Dr. C.S Kulkarni, HOD and Associate Professor, Department of Computer Engineering, VPKBIET, Baramati, Savitribai Phule Pune University, Pune (Maharashtra), India.
Manuscript received on 28 April 2022. | Revised Manuscript received on 21 May 2022. | Manuscript published on 30 May 2022. | PP: 69-75 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99160511622 | DOI: 10.35940/ijitee.F9916.0511622
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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 amounts of data predicted to increase at an exponential rate in the future. The modifications are essential to meet transaction speeds as well as the anticipated growth in data and customer behaviors. The information derived from prior data is extensively relied upon by the majority of companies. One of the primary goals of the suggested system is to identify a reliable sales trend prediction mechanism that is executed using machine learning techniques in order to maximize income. Sales forecasting advises managers about how to manage a company’s employees, working capital and assets. It’s a requirement for strategic planning and decision-making in the corporate world. Reasonable forecasts enable the company to increase market growth while increasing revenue generating. Operations, marketing, sales, production, and finance all use sales predictions as inputs in their decision-making processes. The concept of sales data and sales forecast has been examined in the suggested system. Machine learning algorithms such as GLL (Generalized Linear Model), GBT (Gradient Boosted Trees), and Decision Trees were used to develop the model, and the optimum model for prediction was established based on the results analysis. A best-fit prediction model for anticipating sales trends is offered based on a performance review. The effectiveness and accuracy of the prediction and forecasting approaches used are discussed in the findings. The Gradient Boost Algorithm has been demonstrated to be the best fit model for forecasting and predicting future sales. The sales projection is done using Gradient Boosted Trees, which predicts which product will be sold in what quantity in the future..
Keywords: Data Mining Techniques, Machine Learning Algorithms, Prediction, Reliability, Sales Forecasting.
Scope of the Article: Machine Learning

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F99250511622

IJITEE Journal — Fri, 13 May 2022 07:08:05 +0000

Rainwater Harvesting Potential with the Help of Google Earth Pro and Arc GIS Software
Vijay Kumar¹, Khaleeque U Zaman², Imran Khan³, Love Kumar⁴
¹Vijay Kumar*, Department of Environment Engineering, Quaid-e-Awam University of Science and Technology, Nawabshah Sindh Pakistan.
²Love Kumar, Department of Environment Engineering, University of Florida, USA.
³Khaleeque U Zaman, Department of Environment Engineering, Quaid-e-Awam University of Science and Technology, Nawabshah Sindh Pakistan.
⁴Imran Khan, Department of Environment Engineering, Quaid-e-Awam University of Science and Technology, Nawabshah Sindh Pakistan.
Manuscript received on 29 April 2022. | Revised Manuscript received on 10 May 2022. | Manuscript published on 30 May 2022. | PP: 61-68 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99250511622 | DOI: 10.35940/ijitee.F9925.0511622
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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: Water is a basic source for sustaining our life and environment. Rainwater harvesting has been adopted for a long time and has given a positive impact on our life, agriculture as well as economy. Pressure on water resources will be intensified by climate change. Scarcity of water in Pakistan is a major problem now days for the people. Water from the ground is mostly used in all rural and urban areas of Pakistan; this is a possible reason behind declining of groundwater day by day. RWH technique has been implemented slowly by Pakistani population. Rainwater can be easily collected by using existing structures such as large-sized rooftops, playgrounds, ponds and parks. The Rainwater harvesting practice is quite clean and quality of rainwater compare to groundwater is strongly preferable for many domestic purposes. In this present study, the RWH capability of Engro Limited Fertilizer is calculated for rooftops of warehouses with the help of using ‘‘Google Earth Pro’’ and ‘‘ArcGIS 10.8’’. With an average quantity of annual rainfall in Nawabshah city i.e. 194.855 mm (7.671 inches) within the period (2010 to 2021). Nawabshah is the most suitable city for the practice of rainwater harvesting. The estimated quantity of runoff from rooftops of warehouses of Engro Limited Fertilizer comes out to be 2055466 liters. It is inferred that RWH is the best practical strategy to deflect the present and future water disasters in Pakistan.
Keywords: Rooftop, Rainwater Harvesting, Demand, Storage Tank, GIS
Scope of the Article: Remote Sensing, GIS and GPS

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F99170511622

IJITEE Journal — Thu, 12 May 2022 09:25:48 +0000

Load Based Carrier Aggregation Algorithm for LTE-Advanced
Devaraju J.T.¹, Mohana H.K.², Nethra H.S.³
¹Dr. Devaraju J.T., Department of Electronic Science, Bangalore University, Bengaluru (Karnataka), India.
²Dr. Mohana H.K.*, Department of Electronics, Seshadripuram First Grade College, Bengaluru (Karnataka), India.
³Nethra H.S., Department of Electronics, S.E.A. College of Science, Commerce and Arts, Bengaluru (Karnataka), India.
Manuscript received on 28 April 2022. | Revised Manuscript received on 10 May 2022. | Manuscript published on 30 May 2022. | PP: 53-60 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99170511622 | DOI: 10.35940/ijitee.F9917.0511622
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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: Long Term Evolution (LTE) supports transmission bandwidth upto 20MHz. Nowadays the multimedia rich applications connects billions of users worldwide. These services demands more number of Resource Blocks (RBs) to satisfy the Quality of Service (QoS) requirements and that can increase the data traffic in multifold. This crunch has led the Mobile Network Operators (MNOs) to increase the transmission bandwidth in order to fulfil the QoS requirements of the different services. In Release10, Third Generation Partnership Project (3GPP) provides a provision for MNOs to use Carrier Aggregation (CA) scheme in LTE-Advanced to aggregate upto five Components Carriers (CCs) of existing LTE carriers at the eNB to increase the transmission bandwidth upto 100MHz. Further, this scheme allows a User Equipment (UE) to transmit and receive data packets simultaneously on multiple CCs based on UE capability to meet the service requirements. However, the CC selection and efficient RBs utilization plays a vital role to optimize the system performance. Hence, in this paper using QualNet 8.2 network simulator the Load Based Carrier Aggregation Algorithm (LBCAA) is proposed for LTE-Advanced to aggregate the CCs of the existing LTE carriers at the eNB based on cell load in order to increase the transmission bandwidth to handle the data traffic growth. The simulations results illustrated that the proposed LBCAA for LTE-Advanced outperforms in the achieved average unicast received throughput, average unicast end-to-end delay and jitter as compared to LTE.
Keywords: CA, CC, LBCAA, LTE-Advanced, QoE, QoS, RBs
Scope of the Article: Aggregation, Integration, and Transformation

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F99220511622

IJITEE Journal — Sat, 07 May 2022 09:14:14 +0000

Classification of COVID-19 using Chest X-ray Images with Deep Learning Techniques-CNN & ResNet-18
Kotra Sai Kirthana
Kotra Sai Kirthana*, Department of Computer Science and Engineering, Gitam University, Visakhapatnam (A.P), India.

Manuscript received on 29 April 2022. | Revised Manuscript received on 05 May 2022. | Manuscript published on 30 May 2022. | PP: 49-52 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99220511622 | DOI: 10.35940/ijitee.F9922.0511622
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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: To classify the covid-19 images as infectious or normal, it has been evident that the chest X-ray is a powerful tool to diagnose due to its crucial characteristics of convenience, inexpensiveness and rapid pace. The work aims to determine covid-19 in the infected patients by training models with the dataset using convolutional neural networks (CNN) and ResNet-18 and to draw comparisons in their performances respectively. To handle the dataset by applying various operations to simplify and to make it ready for training, validation and testing procedures of both the algorithms involved. The accuracies obtained on testing CNN and RESNET-18 models are 96.07% and 96.67% respectively. Hence the objective of the work is achieved and the results are obtained by implementing covid-19 classification using chest x-ray images with CNN and resnet-18 models.
Keywords: Coronavirus (COVID-19) Infection, Deep Learning, Convolutional Neural Network (CNN), Resnet-18, Transfer Learning, Chest X-ray Images.
Scope of the Article: Deep Learning

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F98850511622

IJITEE Journal — Fri, 06 May 2022 11:32:17 +0000

Trusted Execution Environments for Internet of Things Devices
Abhilash Kayyidavazhiyil¹, Sheena Kaipacheri²
¹Abhilash Kayyidavazhiyil*, Research Scholar, Liverpool John Moore University, UK.
²Sheena Kaipacheri, Senior Engineer, Digital 14.
Manuscript received on 17 April 2022. | Revised Manuscript received on 03 May 2022. | Manuscript published on 30 May 2022. | PP: 45-48 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F98850511622 | DOI: 10.35940/ijitee.F9885.0511622
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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 trusted execution Environment (TEE) could be a comfy place of a computer’s essential processor that’s designed to shield the most touchy information and operations. TEEs are utilized in an expansion of applications, which incorporates cell gadgets, price processing, and statistics safety. The usage of TEEs is becoming increasingly crucial because the amount of touchy records that are processed and stored electronically continues to develop. TEEs can help guard statistics from being accessed or changed with the resource of unauthorised customers, and can also assist ensure that facts aren’t always compromised at some stage in transmission. TEEs typically applied the employment of specialized hardware that would offer a better degree of protection than software program-most effective solutions. Hardware-primarily based total TEEs can also offer better overall performance and power efficiency than software-handiest solutions. There are some particular TEE implementations to be had, which incorporates Intel’s TXT, ARM’s TrustZone, and Samsung’s KNOX. Each of those implementations has its very personal strengths and weaknesses, so it’s miles more crucial to pick the right TEE on your precise software. reckoning on execution environments are becoming an increasing number of necessities because the amount of touchy facts that’s processed and stored electronically continues growing. TEEs can assist shield facts from being accessed or modified by means of way of unauthorized customers, and might also help make sure that records aren’t compromised at some point of transmission. TEEs normally implemented the employment of specialized hardware, which will offer a far better degree of protection than software program-only answers. To research how this period has been implemented to the exceptional IoT eventualities, which normally address unique characteristics which incorporate device useful resource constraints, we allotted a scientific literature evaluation.
Keywords: Trusted Execution Environment, Internet of Things, Fog Computing, Security, Intel SGX.
Scope of the Article: Internet of Things

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F99180511622

IJITEE Journal — Fri, 06 May 2022 11:19:40 +0000

Gas, Fire & Intruder Detection by an Unmanned Robot using IoT
B. John¹, U. Sam Thomas², B. Kanimozhi³
¹B. John*, Student, Department of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
²U. Sam Thomas, Faculty, Department of Mechatronics Engineering, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
³B. Kanimozhi, Faculty, Department of Mechatronics Engineering, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 26 April 2022. | Revised Manuscript received on 29 April 2022. | Manuscript published on 30 May 2022. | PP: 40-44 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99180511622 | DOI: 10.35940/ijitee.F9918.0511622
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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: This paper presents a robotic device that can detect and fight fire in our homes and workplaces. Its non-human design makes it free to move in the area of fire even if we cannot access it. The device is equipped with sensors that can detect infrared and gas levels. The robot is constrained by an IoT worker utilizing PCs or cell phones. It can also send and receive text messages.
Keywords: Detection of fire, Internet of Things (IoT), Obstacle Avoidance, L293D driver
Scope of the Article: Internet of Things

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F99150511622

IJITEE Journal — Fri, 06 May 2022 11:14:42 +0000

Artificial Intelligence based Stock Market Prediction Model using Technical Indicators
Ketan Bagade¹, Varsha Bhosale²
¹Mr. Ketan Bagade*, M.E. Student, Department of Information Technology, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
²Prof. Varsha Bhosale, Associate Professor, Department of Information Technology & Vice-Principal, Vidyalankar Institute of Technology, Mumbai (Maharashtra), India.
Manuscript received on April 27, 2022. | Revised Manuscript received on April 27, 2022. | Manuscript published on April 30, 2022 | PP: 34-39 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99150511622 | DOI: 10.35940/ijitee.F9915.0511622
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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 indian stock market is highly volatile and complex by nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy & Sell prices are predictable and investor can make above-average profits using efficient Technical Analysis (TA).Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company’s reputation, news, sentiments and other fundamental issues while stock indices are less affected by these issues. In this work, architecture of project is given.As a part of prediction model the Long Short-Term Memory (LSTM), Support Virtual Machine (SVM) are used to predict future prices Stock Technical Indicators(STIs) are used to generate a buy sell signals. The project will be carried on National Stock Exchange (NSE) Stocks of India.
Keywords: Stock Technical Indicators (STIs), Long Short-Term memory (LSTM), Support Vector Machine (SVM), Moving Averages (MA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI).
Scope of the Article: Artificial Intelligence

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F99110511622

IJITEE Journal — Fri, 06 May 2022 11:08:05 +0000

An Advanced Heart Attack Detection and Heartrate Monitoring Alert System using IoT
Sai Tejaswi Guntupalli¹, Lakshmi Harika Potturi²
¹Sai Tejaswi Guntupalli*, Department of Computer Science Engineering, SRM University, Amaravati (AP), India.
²Lakshmi Harika Potturi, School of Department of Computer Science Engineering, SRM University, Amaravati (AP), India.
Manuscript received on 27 April 2022. | Revised Manuscript received on 02 May 2022. | Manuscript published on 30 May 2022. | PP: 29-33 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99110511622 | DOI: 10.35940/ijitee.F9911.0511622
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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: Internet of Things (IoT) plays a vital job in interfacing the encompassing environmental things to the organization and made easy to access those un-internet things from any far-off location. The human death because of heart attack is increasing day by day. Because of today’s human way of life, eating habits, irregular daily schedules, the heart attack issue is becoming predominant. So here we planned a project which enables early recognition of heart attack and provide medical facilities pronto. This mainly centers around early detection of heart attack which thus lessens the human death rate because of heart attack. And by utilizing this gadget, one can screen heart rates which also assists with distinguishing heart attack.
Keywords: Arduino uno, Heart attack, Internet of Things, Pulse, Temperature.
Scope of the Article: Internet of Things

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F99060511622

IJITEE Journal — Sat, 30 Apr 2022 09:54:30 +0000

Health Care Data Analytics – Comparative Study of Supervised Model
Madhu H. K.¹, D. Ramesh²
¹Mr. Madhu H. K.*, Research Scholar, Sri Siddhartha Institute of Technology, Tumkur (Karnataka), India.
²Dr. D. Ramesh, Professor and HOD, Sri Siddhartha Academy of Higher Education, Tumkur (Karnataka), India.
Manuscript received on 26 April 2022. | Revised Manuscript received on 29 April 2022. | Manuscript published on 30 May 2022. | PP: 22-28 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F99060511622 | DOI: 10.35940/ijitee.F9906.0511622
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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 the present pandemic situation, health care data is generated voluminously in an unstructured format posing challenge to technology in perspective of analysis, classification and prediction. The data generated is converted to structured format. Suitability of methodology keeping in mind low computational complexity and high accuracy is a major concern which has emerged as a problem in data science. In this research work real time heart disease data set is considered to evaluate the accuracy of six supervised methods –SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naïve Bayes), LR (Logistic Regression), DT (Decision Tree) and RF (Random Forest). Analysis through ROC curve and confusion matrix predominantly justify RF classifier and LR gives efficient results compared to other methods. This is a preprocessing stage; every researcher has to perform before deciding the methodology to be considered for further processing.
Keywords: SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naïve Bayes), LR (Logistic Regression), DT (Decision Tree) and RF (Random Forest).
Scope of the Article: Data Analytics

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