Mapana Journal of Sciences <p><strong>Mapana Journal of Sciences (MJS)</strong></p> <p><span style="font-weight: 400;">Mapana Journal of Sciences (MJS) publishes high-quality original articles that make a significant contribution to the research areas of both theoretical and applied science.</span></p> <p><span style="font-weight: 400;"><strong>Mapana Journal of Sciences</strong> is included in the <a href=";DiscpName=Arts%20and%20Humanities">UGC-CARE List </a>. (<strong>Group I, Sr.No. 330, Sciences</strong>)</span></p> <p><span style="font-weight: 400;">This journal is an efficient enterprise where the editors play a central role in soliciting the best research papers, and where editorial decisions are reached in a timely fashion. </span></p> <p><span style="font-weight: 400;">The purpose of the journal is coverage of different aspects of Science. We publish original research, review article and research communications from all areas of the Natural and Mathematical sciences. The journal publishes articles, which are focused on existing and new methods, techniques and approaches in the field of Science. MJS publishes contemporary and innovative research, including theoretical, conceptual and empirical research papers. Primarily it has four themes (namely Physical Sciences, Chemical Sciences, Mathematical and Computational Sciences and Biological Sciences) with many sub themes. Each issue has a theme, though fundamental research contributions in the said domain remain welcome.</span></p> <p><strong>The journal does not charge any article processing or article submission charges from the authors.</strong></p> Centre for Publications, CHRIST (Deemed to be University), Bangalore en-US Mapana Journal of Sciences 0975-3303 2D Encoding Convolution Neural Network Algorithm for Brain Tumour Prediction <p>In contemporary times, biomedical imaging plays a pivotal role in addressing various patient-related concerns.&nbsp; Brain imaging, particularly through techniques like MRI, offers valuable insights crucial for surgical procedures, radiotherapy, treatment planning, and stereotactic neurosurgery. To facilitate the accurate identification of cancerous cells within the brain using MRI, deep learning and image classification techniques have been deployed. These technologies have paved the way for the development of automated tumor detection methods, which not only save valuable time for radiologists but also consistently deliver proven levels of accuracy. In contrast, the conventional approach to defect detection in magnetic resonance brain images relies on manual human inspection, a method rendered impractical due to the sheer volume of data This paper outlines an approach aimed at detecting and classifying brain tumors within patient MRI images. Additionally, it conducts a performance comparison of Convolutional Neural Network (CNN) models in this context.</p> F. Paulin P. Lakshmi Copyright (c) 2023 F. Paulin, P. Lakshmi 2023-12-27 2023-12-27 22 Special Issue 2 1 14 10.12723/mjs.sp2.1 A Comparative Analysis of Image Coding Methods A State-of-the-Art Survey <p><strong>Abstract:</strong> As a result of new advanced technology and increased capacity of existing ones, bandwidth requirement is increasing exponentially.Many of the current initiatives in the field of data compression are described by it. The objective of these endeavors is to propose new methods for encoding information sources like audio, images, and video in a manner that reduces the number of bits needed to represent the source content without noticeably compromising the quality. There is a necessity of the new methods that works by reducing the source data without significantly limiting the quality . This is the main intension of these works. In the recent, there has been a significant increase in image compression research, which corresponds with a noteworthy rise in the generation of digital data in the form of images. The objective is to preserve the vital information contained in an image while representing&nbsp; in the fewest possible bits.&nbsp;</p> P R Rajesh Kumar M Prabhakar Copyright (c) 2023 P R RAJESH KUMAR, M Prabhakar 2023-12-27 2023-12-27 22 Special Issue 2 15 36 10.12723/mjs/sp2.2 MedicHub – Disease Detection Using Deep Learning <p>The integration of technology in healthcare is rapidly revolutionizing the sector and transforming the traditional modus operandi that used to be followed into a more efficient and accurate automated system. Machine Learning is a sophisticated technology used to analyze clinical symptoms to predict diseases and deliver accurate diagnoses based on strong evidence. The major advantage of using technology to assist in diagnosis is to understand more about<br />underlying illnesses that are often overlooked while searching for a more severe disease, or when the patient is not in imminent danger. This offers patients a very reliable and accessible alternative for immediate results and also minimizes the risk of errors. Another extremely good utility of technology is withinside the discipline of medical image analysis. CNN are neural networks which are capable of recognizing patterns in pictures and hence must be included in the system to increase its accuracy and efficacy.</p> Nilesh Patil Aaditya Gadiyar Darshan Mehta Harsh Khatri Copyright (c) 2023 Nilesh Patil, Aaditya Gadiyar, Darshan Mehta, Harsh Khatri 2023-12-27 2023-12-27 22 Special Issue 2 37 61 10.12723/mjs.sp2.3 Fuzzy Based Sentiment Classification Using Fuzzy Linguistic Hedges for Decision Making <p>Sentiment analysis is used to identify the attitude, opinions, and emotions of people towards a certain topic or entity. The goal of this paper is to develop a model to do sentiment classification of online product reviews using fuzzy linguistic hedges. The proposed model will be trained on a corpus of reviews, and will be able to classify reviews into a number of sentiment categories, such as positive, neutral, and negative. The proposed model will use fuzzy linguistic hedges to improve the accuracy of the sentiment analysis. The fuzzy linguistic hedges will be used to add context and nuance to the sentiment analysis, and will enable the model to better distinguish between subtle differences in sentiment. The proposed model is tested with microblog electronics dataset. The proposed model is used for making decisions.</p> A Angelpreethi Copyright (c) 2023 A Angelpreethi 2023-12-27 2023-12-27 22 Special Issue 2 63 79 10.12723/mjs.sp2.4 Contemporary aspects and Prospects of Pollution-Free Aviation: Concept of Green Skies with Data Analysis <p>This research paper aims to examine the current state and future of sustainable aviation, electric aviation, and pollution-free aviation. The paper will explore the various technologies and innovations that have been developed to reduce the environmental impact of aviation, including fuel-efficient airplane designs, lightweight materials, and cleaner engines. It will also discuss the potential of electric aviation, which offers a promising alternative to traditional fossil-fueled airplanes. The paper will evaluate the benefits and challenges of these sustainable aviation solutions, and offer recommendations for how the aviation industry can continue to reduce its carbon footprint and minimize its impact on the environment. The findings of this research paper will be of interest to anyone who is invested in creating a more sustainable and environmentally friendly aviation sector.</p> Tiras Jeffrey T Sri Partha Sarathi R Kukatlapalli Pradeep Kumar Copyright (c) 2023 Tiras Jeffrey T, Sri Partha Sarathi R, Kukatlapalli Pradeep Kumar 2023-12-27 2023-12-27 22 Special Issue 2 81 97 10.12723/mjs.sp2.5 Deep Convolutional Neural Network with Image Processing Techniques and Resnet252v2 for Detection of Covid19 from X-Ray Images <div class="page" role="region" data-page-number="1" aria-label="Page 1" data-listening-for-double-click="true" data-loaded="true"> <div class="textLayer"><span dir="ltr" role="presentation">The 2019 coronavirus disease, also known as SARS-CoV-</span><span dir="ltr" role="presentation">2, has emerged as a highly contagious viral infection with a significant </span><span dir="ltr" role="presentation">global impact. It has rapidly spread across various regions, resulting in </span><span dir="ltr" role="presentation">a substantial number of individuals being affected by this disease. Re</span><span dir="ltr" role="presentation">search findings indicate that the rapid and widespread transmission of </span><span dir="ltr" role="presentation">the disease has posed significant challenges for healthcare professionals </span><span dir="ltr" role="presentation">in promptly diagnosing the condition and implementing effective mea</span><span dir="ltr" role="presentation">sures to contain its propagation. The automation of the diagnostic pro</span><span dir="ltr" role="presentation">cedure has emerged as a critical necessity. According to research find</span><span dir="ltr" role="presentation">ings, the implementation of this particular measure has been shown to </span><span dir="ltr" role="presentation">significantly enhance work efficiency while simultaneously safeguarding </span><span dir="ltr" role="presentation">healthcare workers from potential exposure to harmful viruses. Medical </span><span dir="ltr" role="presentation">image analysis is a rapidly growing area of research that offers a promis</span><span dir="ltr" role="presentation">ing solution to address this problem with greater precision. This research </span><span dir="ltr" role="presentation">paper introduces a novel approach for predicting SARS-CoV-2 infection </span><span dir="ltr" role="presentation">using chest radiography images...</span></div> </div> Kavitha Rajalakshmi D Bharathisindhu P Copyright (c) 2023 Kavitha Rajalakshmi D, Bharathisindhu P 2023-12-27 2023-12-27 22 Special Issue 2 10.12723/mjs.sp2.7 Crop Prediction and Recommendation Using Ensemble of DL Models <p><em>Agriculture remains the primary source of income in India and is characterised by a variety of crops, soil types and climatic conditions. This study suggests an additional ensemble model serving to give effective and speedy predictions and recommendations for crops. During the study data from nearly 8 distinct features was collected from various databases and 2201 instances were finalised. The data focussed on climatic conditions such as temperature, rainfall, crop type and soil features, particularly the ratio of nitrogen, potassium and levels of phosphorous. Research indicates that algorithms such as Neural Networks and XGBoost share high effectiveness and accuracy in developing crop yield prediction models. Extensive research conducted shows that the ensemble of XGBoost and MLP Classifier algorithms provide an accuracy of 99.39%. By predicting crop yield based on historical data, the study aims to give sound recommendations on the crops to be cultivated under various weather and soil conditions.</em></p> B. Subbulakshmi M. N. Nirmaladevi R. Rithani Copyright (c) 2023 B. Subbulakshmi, M. N. Nirmaladevi, R. Rithani 2023-12-27 2023-12-27 22 Special Issue 2 10.12723/mjs.sp2.8 Introspection on the Research Avenues of Robotic Process Automation as a Service (RPAaaS) <p>One of the newest business and technology developments is cloud computing, where several users approach the Cloud to complete various tasks. Cloud RPA is a technology that uses robotic process automation on Cloud-native using artificial intelligence. RPA-as-a-service: an automation software or bot that any user with an internet connection can use in the Cloud. It is an automaton self-service in cloud drag-and- drop actions and different GUI as a user-friendly software service. Cloud RPA ensures users automate any process via the Internet on the Cloud and can access it in their browser. RPA enables an intelligent agent to replicate typical manual decisions, such as rule based, well-structured ones involving vast amounts of data in a digital system, and eliminate operational errors.&nbsp;</p> Yashwanth Balan Copyright (c) 2023 Yashwanth Balan 2023-12-27 2023-12-27 22 Special Issue 2 10.12723/mjs.sp2.9 Forest Animal Detection and Alerting System <p>The Internet of Things (IoT) is a physical thing with an ecological connection that is reachable online. IoT is used in many different ways, including smart agriculture, smart healthcare, smart retail, smart homes, smart cities, energy commitment, poultry and farming, smart water management, and other contemporary purposes. In the agricultural industry, man-animal conflict poses a serious problem where a huge amount of resources are lost and human life is put in danger. Due to this, farmers lose their crops, livestock, property, and even their lives. Therefore, it is necessary to regularly monitor this area to prevent the introduction of wild animals. This initiative offered a framework to monitor the situation in this regard. This is done by locating the invader in the area of the field by using a sensor, a camera<br />will then identify the animal, and a text message will be delivered to the farmer through GSM.</p> Karthik M. Copyright (c) 2023 Karthik M. 2023-12-27 2023-12-27 22 Special Issue 2 10.12723/mjs.sp2.10 Imbalanced Multiclass Data Classification Using Combined Data Sampling and Deep Learning Method <p>Multiclass Classification for finding pattern refers to classifying each data to part of the classes or labels that are generally more than two. The foremost challenge in classifying is with imbalanced data&nbsp; &nbsp;that have large portion data known to be majority class, and small portion known as minority class that leads to poor understanding of samples and less accurate results. The existing works discussed Random Upsampling, Random Downsampling, SMOTE methods individually with FeedForward Neural Network and found Random Oversampling gave better results&nbsp; .However, it generates more duplicate data and has less accuracy. Hence , this research work put forward Combined Random Over-Under Sampling approach that was preprocessed prior with Replacing Missing value with mean, Feature selection, Noise Filtering. Meanwhile this work extends the existing FeedForward Neural Network to Deep Learning . The proposed work is implemented in Rapidminer tool, assessed with appropriate evaluation measures for training and testing data individually.</p> Sivasankari Shunmugasundaram Copyright (c) 2023 Sivasankari Shunmugasundaram 2023-12-27 2023-12-27 22 Special Issue 2 10.12723/mjs.sp2.11 Machine Learning based Vehicle Counting and Detection System <p>The study of how machines perceive instead of humans is known as vehicle detection or computer vision object identification. The primary purpose of a vehicle detection system is to identify one or multiple vehicles within the input images and live video feed. The dataset is used to train image processing algorithms for tasks like detection and tracking. To pinpoint the defects and strength of each image processing system, assessment criteria are used to develop, train, test, and compare them. To recognize, track, and count the vehicle in images and videos, the image processing algorithms such as CNN YOLOv3 and SVM are implemented. The main goal and intention of this work is to develop a system that can intelligently identify and track automobiles in still images and moving movies. The results demonstrated that CNN-based YOLOv3 does a&nbsp; good job of detecting and tracking vehicles.</p> <p>&nbsp;</p> <p>&nbsp;</p> Jayamalar T N. Krishnaveni Copyright (c) 2023 Jayamalar T, Dr N.Krishnaveni 2023-12-27 2023-12-27 22 Special Issue 2 10.12723/mjs.sp2.12 Analyzing CT Scan Images towards the Early Detection of Lung Cancer using Medical Images based Edge Feature Preserving CT Scan Medical Image Coder (EZWT - EFPIC) <p>With the current improvements in virtual image processing techniques have received several benefits. Today, all of the scientific techniques produce virtual scientific pictures, through healthcare specialists analyze and diagnose the abnormality. The frequent view of scientific picture processing might also additionally appear simpler; however, it entails many challenges. As the scientific pics are interconnected with human lives, the laptop-aided scientific image processing structures have to be overcautious, if we want to eliminate inaccuracy rates. The utility of medical image processing techniques for the analysis of CT scan images similar to lung cancer cells has been gaining momentum in recent years. This paper discusses the use of a Transform Edge Feature preserving CT scan Medical Image Coder (EZWT - EFPIC) using Computed Tomography (CT) images to help in the early diagnosis of lung cancer. We discuss and explore the design and significance of an EZWT-EFPIC-CT image-processed model in cancer diagnosis.</p> C. Thirumoorthi Manikandaprabhu P P. V. Praveen Sundar Copyright (c) 2023 C. Thirumoorthi, Manikandaprabhu P, P. V. Praveen Sundar 2023-12-27 2023-12-27 22 Special Issue 2 261 272 10.12723/mjs.sp2.14 A Forecasting Model of Predicting Cryptocurrency Price <p>Nowadays the quest for electronic payments has created a huge ambit among academicians and businesspeople. At the same time, transactions are repressed because of the intervention of third parties. To overcome this situation, the great as well as the puzzling imposter arose which is now the area of interest called cryptocurrency. Bitcoins, Ethereum, and ripple are some embodiments of cryptocurrency. Investors do not always have a bed of roses with cryptocurrency as the frequent oscillation of prices is hard to forecast. The paper here deals with the forecasting of cryptocurrency prices by using data mining algorithms such as Bagging, K-NN, Linear Regression, and Support Vector Machine. The outcome specifies the accuracy value gained from the cryptocurrency forecasting model from which we can predict the price of the cryptocurrency.</p> Vignesh Ramamoorthy H Spelmen Vimalraj Santhanam V. Vibithrapriya R.G. Harshini Copyright (c) 2023 Vignesh Ramamoorthy H, Spelmen Vimalraj Santhanam, V. Vibithrapriya, R.G. Harshini 2023-12-27 2023-12-27 22 Special Issue 2 273 282 10.12723/mjs.sp2.15 Empirical Analysis And Perception of Health Disparities in Rural People with Special Reference to Anaikatti Village Coimbatore <p>The goal of the present research is to comprehend the existing state of health among residents of Anaikatti village in the Coimbatore district. The most prevalent diseases and signs of health problems have been covered. The public' perceptions of health concerns and their causes are also examined. The study takes a qualitative method. The research is based on real- time data gathered from a poll of Anaikatti Village inhabitants.&nbsp; To analyse primary data, statistical techniques such as percentage analysis, descriptive statistics, and ANOVA are applied. The findings show that some areas of the neighbourhood have inadequate drinking water and sanitary care. Legislators and health-care practitioners, with the help of non-governmental organisations, must take adequate actions to ensure safe drinking water and sanitary facilities. Frequent health camps, free distribution of medications for common symptoms, actions to increase the nutritional content of food consumed and frequent doctor visits may improve overall health.&nbsp;</p> G. Maria Priscilla M. Hemalatha C. Deepa Copyright (c) 2023 G.MARIA PRISCILLA, M HEMALATHA , C DEEPA 2023-12-27 2023-12-27 22 Special Issue 2 283 295 10.12723/mjs.sp2.16 An Intelligent Facial Recognition System using Stacked Auto Encoder with Convolutional Neural Network (CNN) Approach <p>The act of identifying an emotional feeling&nbsp; is described as facial expression.&nbsp; one of the effective techniques for interperson communication. They serve as indications that regulate interactions with those around. As a result, they are crucial in creating effective relationships.Facial expression recognition system to identify the expressions by evaluating the changes in facial characteristics and extracting features from facial images. This system&nbsp; essential for enhancing computer-human interaction. The majority of facial emotion recognition research mainly relies on&nbsp; reference face model and well known facial landmarks. Due to&nbsp; intricacy of the face musculature, finding the most noticeable facial landmarks can be difficult and requires physical intervention for improved accuracy. So, this research work provides&nbsp; new dimension to deal with the above issues by proposing a Stacked Auto-Encoder with Convolutional Neural Network based approach that does not rely on the landmarks or a reference model. The proposed approach outperforms the existing techniques.</p> N. Mahendiran Copyright (c) 2023 N. Mahendiran 2023-12-27 2023-12-27 22 Special Issue 2 297 311 10.12723/mjs.sp2.17 Comparative Study for Image Fusion using Various Deep Learning Algorithms Anna Saro Vijendran Kalaivani Ramasamy Copyright (c) 2023 Anna Saro Vijendran, Kalaivani Ramasamy 2023-12-27 2023-12-27 22 Special Issue 2 313 334 10.12723/mjs.sp2.18 Unlocking the Future: DNA Encryption for Secure and Efficient Massive Data Storage <p><strong>DNA has emerged as a promising medium for digital data storage because of its high density, longevity, as well as energy efficiency. However, the security provided by &nbsp;DNA storage systems remains a concern, particularly as the technology is adopted for sensitive data applications. DNA encryption offers a potential solution to this problem by encoding the stored data in a secure and reversible manner. In this paper, a new DNA encryption for storage applications by editing or creating a new DNA sequence to store big data for archival purposes in an encrypted format to provide security, is proposed. It is concluded that DNA encryption is a promising approach for securing digital data in DNA storage systems, and there is requirement for further research to optimize the performance and reliability of this technology. </strong></p> Jayapriya J Vinay M Shorya Rawal Rudraksh Gohil Copyright (c) 2023 Jayapriya J, Vinay M, Shorya Rawal, Rudraksh Gohil 2023-12-27 2023-12-27 22 Special Issue 2 335 348 10.12723/mjs.sp2.19 Blood Management System Using Blockchain <p>Blood is a crucial constituent within the human body that is irreplaceable for human life, it supplies supplements and oxygen to all the cells, due to this fundamental part, the requirement for a decentralized blood bank has been introduced in this paper. Manual frameworks as compared to computerized frameworks are time-consuming, exorbitant, and may regularly contain human errors. Moreover, they are helpless to the single point of disappointment issue due to centralization and may lack privacy and security features. This research paper explores the usage of a blood management system based on blockchain technology. The current blood management systems confront challenges such as donor-recipient anonymity, traceability, and straightforwardness. These issues can be tended to by utilizing blockchain, which gives a decentralized and secure database for data administration. The suggested system makes use of blockchain <br />to handle and preserve data from blood banks, such as donor details, blood type, and availability. </p> Nidhi Suvarna Akshay Agarwal Aks Manish Jain Shubham Nilesh Rathod Soham Paresh Shah Copyright (c) 2023 Nidhi Suvarna, Akshay Agarwal, Aks Manish Jain, Shubham Nilesh Rathod, Soham Paresh Shah 2023-12-27 2023-12-27 22 Special Issue 2 349 361 10.12723/mjs.sp2.20 E-PHARMACY IN INDIA: LESSONS FROM THE PANDEMIC AND THE WAY FORWARD <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <strong>Abstract</strong>: E-Pharmacy is one of the essential services that can bring out transparency and ease of buying medicines to the customers. Though the application of internet in the areas of healthcare is intensifying and the online pharmacies serve a good cause, it has not been used as extensively in India. In this study we have focused in detail about the reasons why online pharmacy is not used - from a sample size of 100 respondents and have found that it was because of the high risk of misuse of drugs especially where there are no governing online pharmacy laws, lack of awareness and poor logistics, we have also analysed the other challenges that are faced by the society while using the existing e-pharm apps. Based on our constructs from the inferential and descriptive analysis, which was validated with a sample size of 100, we have put forth a set of suggestions that can make significant improvements in the e-pharmacy sectors and boost its usage.</p> <p>&nbsp;</p> Maheswari K Copyright (c) 2023 Maheswari K 2023-12-27 2023-12-27 22 Special Issue 2 363 375 10.12723/mjs.sp2.21 The Early Prediction of Liver Problems Using Knowledge Mining Techniques <p><strong>&nbsp;</strong>Knowledge Mining methodologies in health maintenance bump into radiology and chatterbots. These results however can shape the patterns in different sectors of patients with their symptoms. I foresee some of the Knowledge Mining algorithms are capable of identifying the possibilities or the probabilities of getting cancer, and imaging solutions and orphan diseases or specific types of pathology. The algorithms of knowledge mining are exists as Deep learning methodologies that has started emerging as a prominent technique in providing medical professionals with insights that lets them predict issues early on, thereby delivering far more personalized and relevant patient care.</p> <p>&nbsp;</p> <p>&nbsp;</p> Kowsalya S Copyright (c) 2023 Kowsalya S 2023-12-27 2023-12-27 22 Special Issue 2 376 396 10.12723/mjs.sp2.22 A Survey on Spoofing and Selective Forwarding Attacks on Zigbee based WSN <p>The main focus of WSN is to gather data from the physical world. It is often deployed for sensing, processing as well as disseminating information of the targeted physical environments. The main objective of the WSN is to collect data from the target environment using sensors as well as transmit those data to the desired place of choice. In order to achieve an efficient performance, WSN should have efficient as well as reliable networking protocols. The most popular technology behind WSN is Zigbee. In this paper a pilot study is done on important security issues on spoofing and selective forwarding attack on Zigbee based WSN. This paper identifies the security vulnerabilities of Zigbee network and gaps in the existing methodologies to address the security issues and will help the future researchers to narrow down their research in WSN.<br>Keywords: Zigbee, WSN, Protocol Stack, Spoofing and Selective Forwarding.</p> Nethra Pingala Suthishni D D. Shanmugapriya Copyright (c) 2023 Nethra Pingala Suthishni D, D. Shanmugapriya 2023-12-27 2023-12-27 22 Special Issue 2 397 419 10.12723/mjs.sp2.23