Volume-9 Issue-2, July 2020 – International Journal of Recent Technology and Engineering (IJRTE)

E4975018520

IJRTE Journal — Thu, 20 Jan 2022 09:05:14 +0000

Cloud Computing Security Management using CSP
Rasha Rokan Ismail¹, Taha Mohammed Hasan²
¹Rasha Rokan Ismail, Department of Computer Science, Diyala University, Iraq.
²Taha Mohammed Hasan, Department of Computer Science, Diyala University, Iraq.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1230-1236 | Volume-9 Issue-2, July 2020. | Retrieval Number: E4975018520/2020©BEIESP | DOI: 10.35940/ijrte.E4975.079220
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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 cloud was defined by lots of experts, yet the NIST (National Institute of Standards and Technology) has presented the definition: “a model for enabling comfortable, on-demand network access to a shared pool of configurable computing resources The aim of this paper is a model for safe data sharing on cloud computing with intension to provide data confidentiality and access control over shared data, it also removes the burden of key management and files by users. The system also supports dynamic changes of membership and enables clients to reach the data they require even when the owner does not exist in the system. In the proposed system, a new security system is introduced, it provides a mechanism through which communication is safely achieved as well as it protects users and their hidden information from unauthorized users. The Entities in Proposed System consist of three parts: CSP, Users (owner ,clients ) and TPA , in this paper the focus will be on the CSP and the users. The proposed system are provides data confidentiality, access control of share data, removes the burden of key management and file encryption/decryption byusers, support dynamically of users membership. The use of a digital signature ensures the integrity and confidentiality of sharing data sent by users so that it cannot be read by the recipient TPA as it encrypts, sends a new encrypted signature and sends it to the CSP so that it cannot read its content CSP proved to be effective in the security of cloud computing.
Keywords: NIST, CSP, TPA.

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A1625059120

IJRTE Journal — Thu, 20 Jan 2022 07:45:35 +0000

Precision Farming and Smart Irrigation using IoT
Ponlakshmi P¹, Vidhya Saraswathi P²
¹Ponlakshmi P, Student, Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhnagar District, India.
²Vidhya Saraswathi P, Assistant Professor, Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhnagar District, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1226-1229 | Volume-9 Issue-2, July 2020. | Retrieval Number: A1625059120/2020©BEIESP | DOI: 10.35940/ijrte.A1625.079220
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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: Agriculture is the spine of Indian Economy. It mainly depends on Irrigation. The Internet Of Things is used to farmers are easier to monitor and control water possessions. This paper proposed, IoT architecture customized for smart irrigation application. Arduino board is used to communicate a variety of sensors like ultrasonic, soil moistur and light sensors. This work managed to decrease the expenditure, diminish devastate water and diminish substantial individual edge. Relay is developed to organize the switching of solenoid. Also, the scheme preserved to measure the soil moisture. It controls the solenoid valve according to human. Graphical User Interface (GUI) connected withAndroid application to motivate watering movement. SMS alert moreover sent to the home user in critical situations.
Keywords: IoT, Soil moisture sensor, ultrasonic sensor, light sensor, Arduino board.

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B4164079220

IJRTE Journal — Thu, 20 Jan 2022 07:43:13 +0000

An Advanced Machine Learning Model for Disease Prediction
Ayushi Sharma¹, Shipra Shukla²
¹Ayushi Sharma, CSE, Amity University, Noida, India.
²Dr. Shipra Shukla, Asst. Prof., CSE, Amity University, Noida, U.P., India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1220-1225 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4164079220/2020©BEIESP | DOI: 10.35940/ijrte.B4164.079220
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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 settle on right choices and pass on about vital control measures, numerous flare-up expectation models for anticipating COVID-19 are getting utilized all round the world. Straightforward conventional models have indicated extremely less precision rate for future forecast use, because of more significant levels of vulnerability and absence of proper information. Among the different machine learning model algorithms contemplated, an ensembled model was seen as giving the best outcomes. Because of the multifaceted nature of the virus’s temperament, this research paper recommends machine learning to be an extremely helpful gadget to consider in case of the ongoing pandemic. This paper gives a colossal benchmark to call attention to the probability of machine learning to be utilized as an instrument for future exploration on pandemic control and its timely prediction. Moreover, this paper delineates that the best prompts for pandemic prediction are frequently comprehended by combining machine learning, predictive analytics and visualisation tools like Tableau. The main purpose of this research is to build a perfect ML model prototype which can be later used when access to appropriate dataset (which is both large and consists of many different features) is available. Also, the secondary aim is to automate the process of reporting so as to facilitate quicker action by the concerned authorities, and help common people reach out to the correct destination for treatment or help. Furthermore, the Tableau analysis performed on the dataset is to provide more analytical depths for people with expertise in the medical domain.
Keywords: COVID-19, machine learning, predictive analytics, Tableau.

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B3896079220

IJRTE Journal — Thu, 20 Jan 2022 07:38:55 +0000

Factors Influencing Rain-Fed Agricultural Land Abandonment in Mnquma and Mbashe Municipalities, Eastern Cape
Mzuyanda Christian¹, Phiwe Jiba², Mdoda Lelethu³
¹Mzuyanda Christian, Senior Lecturer, Deaprtment of Agricultural Economics and Extension, North West University.
²Phiwe Jiba, Department of Agricultural Economics and Extension, North West University.
³Lelethu Mdoda, Lecturer, Department of Agricultural Economics and Extension, University of Fort Hare.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1213-1219 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3896079220/2020©BEIESP | DOI: 10.35940/ijrte.B3896.079220
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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: Agriculture is one of the imperative segments in the South African economy and it remains the imperative sector for livelihood generation. However, it has been observed that farmers are gradually giving up agriculture in favor of non-agricultural activities. This paper examines the factors influencing agricultural land abandonment in Mnquma and Mbashe Municipalities in Eastern Cape Province. Surveys of 158 semi-structured field interviews were conducted to capture household characteristics, location and farming practices in the study areas. The findings show that limited access to funding, level of education, household size and farming experience seem were the main factors influencing abandonment of rain-fed agricultural land. Another notable reason for agricultural land abandonment was the lack of resources such as storage facilities, transport and access to lucrative markets. Therefore the study recommended that government policies should go beyond supporting primary production and focus also on value adding activities. One of the findings indicated that these areas are dominated by older people (55 years). The study also recommended that focus should be directed on more manageable small plots for older people to increase food production.
Keywords: Rain-fed, Agricultural land, Abandonment, Probit regression, Eastern Cape.

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F9847038620

IJRTE Journal — Thu, 20 Jan 2022 07:34:15 +0000

An Efficient Encoding Technique based on Chaotic Cryptography for Digital Image
Deepak Kourav¹, Taslima Ahmed², Prashant Mavi³
¹Dr. Deepak kourav, Associate Professor, IIMT College of Engineering Greater, Noida UP, India.
²Dr. Taslima, Associate Professor, IIMT College of Engineering Greater, Noida UP, India.
³Mr. Prashant Mavi, Associate Professor, IIMT College of Engineering Greater, Noida UP, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1208-1212 | Volume-9 Issue-2, July 2020. | Retrieval Number: F9847038620/2020©BEIESP | DOI: 10.35940/ijrte.F9847.079220
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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: Presently advanced India notoriety, associations are proposing various structures concentrating on computerized encoding procedures. Because of the simplicity of replicating, altering, and altering of computerized archives and pictures has prompted encoding the data required for transmission and capacity. It clear that the connection between’s the picture pixels to its neighborhood district is high, decreasing relationship between’s the pixels esteem makes it hard to figure for the first picture and along these lines enhance the security. This paper presents a novel picture encoding strategy which at first improves the picture based on exchanging dim codes and pixel blast. The pixel blast utilizes very much characterized key that switches between the dim code of the picture pixels. Exploratory outcomes would demonstrate that the proposed pixel blast is sufficient for fractional encoding and upgrades security of the information. Further, it could likewise bolster as a deadly implement for any current calculation.
Keywords: Encoding, Gray-Code, Pixel Blast, Authentication.

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B3727079220

IJRTE Journal — Thu, 20 Jan 2022 07:30:10 +0000

Impact of Artificial Intelligence on Customer Experience
Bipasha Rajkhowa¹, Aniket Das²
¹Bipasha Rajkhowa , Student, Symbiosis Institute of Digital & Telecom Management, Symbiosis International (Deemed University), Pune, India.
²Aniket Das, Student, Symbiosis Institute of Digital & Telecom Management, Symbiosis International (Deemed University), Pune, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1237-1242 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3727079220/2020©BEIESP | DOI: 10.35940/ijrte.B3727.079220
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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: With the increasing competition in the market today, the customers have a variety of options to choose from. The biggest challenge faced today by any business is to understand and deliver the exact requirements of the customers to retain the existing customer base and acquire new customers. The key to this is meeting and surpassing customer expectations leading to the goal of customer satisfaction through enhanced customer experiences. Upcoming technologies like Artificial Intelligence provide widespread opportunities to understand dynamic customer behavior and trends. However, not much exploration is done in academic research as to what factors primarily impact customer experience and the emerging significance of AI in this domain. Hence, the objective of this study is to understand the impact of artificial intelligence on customer experience. The paper adopts a quantitative approach wherein a survey was conducted across 207 participants to understand the impact of AI-enabled chatbots to deliver better personalization, quality of service, and hassle-free service to achieve better customer experience. The study provides implications to academicians as it contributes to the literature on the impact of AI on customer experience, helps practitioners in exploring and analyzing the various useful aspects of such emerging technologies in reshaping market trends, the companies and the society at large by providing a better experience to the customers, thus enabling a healthier customer relationship.
Keywords: Artificial Intelligence, Customer Experience, Chatbots, Hassle free service, Personalization, Quality of Service.

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B4106079220

IJRTE Journal — Thu, 20 Jan 2022 07:22:15 +0000

Allocation of PV unit in Distribution Network using Analytical Method
Ritesh Kumar Rai¹, Anand Kumar Pandey², Anshu Parashar³, Sujatha K S⁴
¹Ritesh Rai*, EEE, Engineering Management Technical Campus, Greater Noida (UP) India.
²Anand Kumar Pandey,EEE, JSS Academy of technical education, Noida (UP) India.
³Anshu Parashar, EEE, Engineering Management Technical Campus, Greater Noida, (UP), India
⁴Dr. Sujatha K S, EEE, JSS Academy of technical education, Noida (UP) India.
Manuscript received on June 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1200-1207 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4106079220/2020©BEIESP | DOI: 10.35940/ijrte.B4106.079220
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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: Inappropriate selection of location and corresponding size of Distributed Generator (DGs) in electrical network may have increased power losses in the system. Application of incorporating DG in system has eased the problem of high power losses, voltage stability, low reliability and poor power quality. This paper suggests a simple and efficient load flow technique known as direct load flow method to find the optimal allocation of Type-3 DG in the distribution system. The presented method was developed and tested in two distribution networks with varying size and complexities and the effect of size and location of DG with respect to real power losses while maintaining the voltage profile of system within limits is examined with verification and discussed in detail.
Keywords: Power system optimization, Distribution system, Optimization techniques. Location and sizing.

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B4119079220

IJRTE Journal — Thu, 20 Jan 2022 07:09:46 +0000

Analysis on Detecting a Bug in a Software using Machine Learning
Rashmi P¹, Prashanth Kambli²
¹Rashmi P, Department of CSE, MSRIT, Bangalore, India.
²Prashanth Kambli Assistant professor, Department of MSRIT, IS&E, Bangalore, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1195-1199 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4119079220/2020©BEIESP | DOI: 10.35940/ijrte.B4119.079220
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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 scenario, it is very essential in the development phase of a software, predicting a bug and to obtain a successful software. This can be achieved only through predicting some of the faults in the earlier phase itself such that, it can lead to have a reliable, efficient and a quality software. The challenging task here is to have a well sophisticated model that can predict the bug leading to a cost-effective software. In order to achieve this, few machine learning algorithms are used that produce accuracy with trained and test datasets. A variety of machine learning methodologies have been developed to learn and detect a bug in a software. In this paper, we perform the analysis on detecting a bug in a software using machine learning methods which is very much useful for Software Industries. It summarizes the existing work on detecting a bug in a software by providing the information about various methods involved in bug prediction and points out at the accuracy obtained by the existing methods, advantages, and the drawbacks while working with bug prediction.
Keywords: Software Development, Software Bug Prediction, Quality Software, Machine Learning, Accuracy.

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B4115079220

IJRTE Journal — Thu, 20 Jan 2022 07:06:20 +0000

Smart Water Distribution System using Machine Learning and IoT
Aashika Perunkolam¹, Makshi P Baskaran², Sarrthak Tripathi³, Om Prakash Sahu⁴
¹Aashika Perunkolam, Electronics and Communication Engineering, Vellore Institute of Technology, Chennai, India.
²Makshi P Baskaran, Electronics and Communication Engineering, Vellore Institute of Technology, Chennai, India.
³Sarrthak Tripathi, Electronics and Computer Science Engineering, Vellore Institute of Technology, Chennai, India.
⁴Om Prakash Sahu, Electronics and Computer Science Engineering, Vellore Institute of Technology, Chennai, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1189-1194 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4115079220/2020©BEIESP | DOI: 10.35940/ijrte.B4115.079220
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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 Management includes four major processes, namely, estimating the amount of water readily available to be distributed, the measurement of the quality of water, distribution of water to different sectors of the city based on the quality and finally to provide a platform to monitor this distribution from anywhere and by anyone. All these processes, are currently treated as separate modules, but the integration of these four models, enhances water conservation and creates a social awareness since the proposed cloud platform can be accessed by everyone, and they are made aware, in advance, about the quality and amount of water they are going to get for the week, so that they can use the water wisely. In this paper we discuss our new and improved proposed model which not only integrates the existing four modules but also optimizes the distribution path based on algorithms for the fastest coverage. This is turn provides a short and concise solution to water management which is more user friendly and can reach more people, hence spreading more awareness. The lack of coordination between the current quality measurement and distribution system calls for an integrated system. This system predicts the rainfall to prepare the system for the amount of softener required to soften the water which acquires calcium and magnesium as it makes its way into other natural rivers. This water management system can be setup both on a small scale and a large scale. The smaller water management system setup in villages can be interconnected to make a larger water management system that can be centrally controlled from cities which helps in ensuring water distribution even in the smallest towns and villages.
Keywords: Forecasting, Knowledge Acq

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B4167079220

IJRTE Journal — Thu, 20 Jan 2022 07:00:19 +0000

Implementation of Practice Learning During the Covid-19 Pandemic
Nurhasan Syah¹, Syaiful Haq², Nizwardi Jalinus³, Ambiyar⁴
¹Nurhasan Syah, Civil Engineering, Engineering Faculty Universitas Negeri Padang, Padang, Indonesia.
²Syaiful Haq*, Civil Engineering, Engineering Faculty Universitas Negeri Padang, Padang, Indonesia.
³Nizwardi Jalinus, Mechanical Engineering, Engineering Faculty Universitas Negeri Padang, Padang, Indonesia.
⁴Ambiyar, Mechanical Engineering Faculty Universitas Negeri Padang, Padang, Indonesia.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1184-1188 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4167079220/2020©BEIESP | DOI: 10.35940/ijrte.B4167.079220
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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 research was done because of the covid-19 pandemic. One regulation impact of this pandemic is social distance, so it disturbed the implementation of learning because students did not allowed to learn face to face in a room or at campus. They must learn from home by online learning. This regulation was not a problem for some courses, but it was not easy for practice course because students need tools and do demonstration in laboratory or workshop. This descriptive qualitative reseach was done to observed people who involved in specific teaching method course in the mechanical engineering department of Universitas Negeri Padang (UNP). The key of this research are about data observation, triangulation, careful examination, then data reduction, presentation, and conclusion. The research result stated that; 1) there was no changes in the substance of the learning planning during covid-19, 2) the implementation of practice learning was done by fully learning during covid-19, student and lecture did communication by using whatsapp, e-mail, zoom cloud meeting, UNP’s e-learning, and others tools such as youtube, 3) the assessment of learning was done by testing and observation.
Keywords: Implementation, Online Practice Learning, Pandemic, Covid-19.

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