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Image Pre-Processing Algorithms for the Quality Detection of Tea Leaves
This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python. 2022 IEEE. -
Design and Verification of a Novel Anchor Shaped Double Negative Metamaterial Unit Cell
In this manuscript, a novel anchor-shaped double negative metamaterial is proposed. The structure is designed to resonate at 2.45 GHz. The unit cell is designed on a 1.6 mm thick FR4 substrate having a dielectric constant of 4.4, and simulated using Ansys HFSS. The unit cell exhibits a double negative behavior and negative refractive index behavior. The robust and popularly used Nicolson-Ross-Weir and Transmission-Reflection methods were implemented on MATLAB to extract and validate the metamaterial characteristics. This novel metamaterial unit cell covers 1 GHz to 4.8 GHz which is one of the most extensively researched and employed bands of the electromagnetic spectrum. The bandwidth performance of this new structure for double negative behavior is compared to other unit cells. It shows better performance with comparable size and outperforms the other geometries. This metamaterial is well-suited for a wide range of applications like wireless communication, biomedical applications in ISM (2.4 GHz) band and 5G communication services in the sub-6 GHz range. 2022 IEEE. -
Study on 5G Massive MIMO Technology Key Parameters for Spectral Efficiency Improvement Including SINR Mapping on Rural Area Test Case
Massive MIMO is one of the key disruptive technologies in 5G which offers significant change in the core network architecture and channel modeling compared to the previous wireless communication standards. There are many research works currently focusing on implementing Massive MIMO network in different channel propagation models. ITU, 3GPP and IMT consortium deliver timely 5G LTE releases and taken as benchmark documents by various telecom companies and universities to set up testing, trials and hardware deployments. However, without optimization on spectral efficiency parameter, the specifications proposed by 5G in terms of improvement in data rate or throughput could be difficult to achieve. This paper initially provides an in-depth study on spectral efficiency estimation and optimization in Massive MIMO by investigating different research papers. From these papers, list of parameters involved in spectral efficiency are identified, such as, fading characteristics, power or energy efficient parameters, standard deviation, angle of arrival factors in antennas installed in base stations and many others. The author however concludes with the best selection of constraint optimization parameters to improve the spectral efficiency taking into account of its simple design and major impact on the improvement in the result by taking downlink scenario of a simulation environment using 5G Massive MIMO network. SINR mapping of standard Rural Macro test scenario adopted from M 2314, LTE release 17 of 5G framework is simulated in this research paper. 2022 IEEE. -
Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation in Cloud-SDN Environment
Cloud computing (CC) remains as a promising environment which offers scalable and cost effectual computing facilities. The combination of the SDN technique with the CC platform simplifies the complexities of cloud networking and considerably enhances the scalability, manageability, programmability, and dynamism of the cloud. This study introduces a novel Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation (MEDR-DDoSAD) technique in Cloud-SDN Environment. The major aim of the presented technique lies in the recognition of DDoS attacks from the cloud-SDN platform. The MEDR-DDoSAD technique transforms the input data into images and the features are derived via deep convolutional neural network based Xception model. 2022 IEEE. -
Paradigm of Green Technologies in Hospitality Industry and its Sustainability Analytics
The function of the study is to investigate the customer attitude towards the sustainable or Green Technologies adopted by the hospitality industry and how this has changed the purchase intentions of the customer. This also explores the disposition of people to pay for and repeat these services and how the new green practice and techniques have changed the brand image. The data was collected through a monitored survey from 448 people across India. The conceptual framework that was formulated is tested during structural equational modelling. As a result of the study, it was found that, green purchase intentions are significantly influenced by the attitude they have towards Green Technologies/services. All stakeholders in the hospitality industry in India will find this paper useful. 2022 IEEE. -
Fake News Detection and Classify the Category
Everyone depends on numerous sources of E-news in today's world when the internet is ubiquitous. Online content abounds, especially social media feeds, many of which are unreliable and may not always be factual. For people to utilise social media platforms like Facebook, Twitter, and others, fake news is a topic that may be studied through Natural Language Processing techniques. Using ideas from natural language processing and machine learning applied to social media, our goal in this work is to conduct categorization of different news items that are available online. Our intention is to empower the user to utilise NLP (Natural Language Processing) methods to identify 'fake news,' which refers to misinformed material that may be categorised as genuine or false using software like Python. The model focuses on identifying false news sources based on several articles from a website, categorising the news as false or true, and determining its veracity using unreliable sources like scikit-learn and NLP for textual analysis of the website distributing the news. When a source is identified as a publisher of false news, which can be predicted with high vectorization and also suggested using the Python scikit-learn module to do tokenization and feature development, biased viewpoints may be identified and categorised in any subsequent articles from that source. 2022 IEEE. -
Perception and Practices of EdTech Platform: A Sentiment Analysis
Virtual and digital learning being the new normal, pandemic outburst and unexpected disruption in the functioning of educational services have paved way for online learning services. Considering the fast-Track growth of the education technology (EdTech) industry, in order to sustain, it is imperative for the industry to understand the underlying issues by capturing the end users' perception. The primary purpose of this research is to examine the perception of users towards EdTech platforms A sample of 600 reviews regarding three major EdTech platforms were scraped from MouthShut.com as textual data and analysed using lexicon-based method. The polarity of the sentiments pertaining to the reviews of different platforms was analysed using sentiment analysis. Furthermore, the topic modelling on the reviews was performed using natural language programming. The results revealed a positive sentiment of users towards the EdTech services and platforms. The most influential factors are faculty expertise, interface user-friendliness, syllabus, and pricing model. Our findings help EdTech service providers to understand which factors are driving this dramatic shift in student behaviour so they may develop better strategies to attract and retain consumers. Despite the rise in EdTech platform popularity, this is the first study to investigate perception of EdTech users comprehensively. 2022 IEEE. -
Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study
Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. 2022 IEEE. -
Enhanced Automated Oxygen Level controller for COVID Patient By Using Internet of Things (IoT)
The Internet of Things (IoT) shall be merged firmly and interact with a higher number of altered embedded sensor networks. It provides open access for the subsets of information for humankind's future aspects and on-going pandemic situations. It has changed the way of living wirelessly, with high involvement and COVID-related issues that COVID patients are facing. There is much research going on in the recent domain, like the Internet of Things. Considering the financial-economic growth, there isn't much significance as IoT is growing with industry 5.0 as the latest version. The newly spreading COVID-19 (Coronavirus Disease, 2019) will emphasize the IoT based technologies in a greater impact. It is growing with an increase in productivity. In collaboration with Cloud computing, it shows wireless communication efficiently and makes the COVID-19 eradication in a greater way. The COVID-19 issues which are faced by the COVID patients. Many patients are suffering from inhalation because of lung problems. The second wave attacks mainly on the lungs, where there is a shortage of breathing problems because of less supply of oxygen (insufficient amount of oxygen). The challenges emphasized as proposed are like the shortage of monitoring the on-going process. Readily being active in this pandemic situation, the mentioned areas are from which need to be discussed. The frameworks and services are given the correct data and information for supply of oxygen to the COVID patients to an extent. The Internet of Things also analyzes the data from the user perspective, which will later be executed for making on-demand technology more reliable. The outcome for the COVID-19 has been taken completely to help the on-going COVID patients live, which can be monitored through Oxygen Concentration based on the IoT framework. Finally, this article discusses and mentions all the parameters for COVID patients with complete information based on IoT. 2022 IEEE. -
Antecedents of Adoption of Peer to Peer (P2P) Lending-A Fintech Innovation in India
This study examines the association between adoption variables and behavioural intention (BI) to adopt Peer to Peer (P2P) lending technology platform in India. A critical review of literature on technological and personal adoption factors led to development of the theoretical framework using multiple technology adoption models. Results support the generalizability of technology adoption readiness (AR), a parsimonious higher-order construct for the use and acceptance of technology context In addition, a personal antecedent, personal innovativeness (PIIT) was shown to positively affect behavioural intentions and technology adoption readiness. 2022 IEEE. -
MRSP-Multi Routing Systems and Parameter Explanations to Build the Path in Underwater Sensor Network
The underwater network is currently widely used to locate moving objects beneath the sea, monitor marine security, and detect changes in the sea water. A large number of sensors, as well as a precise methodology, are necessary to detect changes in sea depth. The protocol should be revised in response to environmental and chronological changes. The sensor should have been designed with multiple knowledge to route packets in order to optimise transmissions. Because the node will choose the best route based on the circumstances, especially in an underwater network, the paper MRSP - multi routing systems and parameter validations to create the path in an underwater sensor network is discussed in the multi routing knowledge sensor operations, energy saving systems, redundancy reduction, and so on. All of these measures, combined with secure transmission with trusted neighbour selection, result in safer transmissions and more accurate path selection. 2022 IEEE. -
Transforming towards 6G: Critical Review of Key Performance Indicators
With the experiences acquired upon the successful implementation of 5G networks academia, researchers, and industry are envisioning the need for 6G networks. The vision of the 6G communication network is supposed to completely assist the creation of a Ubiquitous Intelligent Mobile Society. Already 5G technologies are in place and still few extended features of 5G are continuously being introduced. Even though the 6G communication network is expected to have greater capabilities than the existing 5G, there are no clear specifications on how far these capabilities shall be capitalized in 6G. The 6G technologies shall move past ordinary mobile internet services and advance to support ubiquitous Artificial Intelligent (AI) services from the network's core to end-to-end service devices/applications. The architecture, protocols, and operations which are the primary constituents of the 6G network shall implement AI technologies for self-optimization and actualization. This article brings an all-inclusive deliberation of 6G based on an assessment of preceding generations' evolving technology developments. 2022 IEEE. -
Multilingual Sentiment Analysis of YouTube Live Stream using Machine Translation and Transformer in NLP
YouTube has become one of the all-inclusive video streaming sources on the internet. Today, the news is streamed on YouTube, marketing of a product is done live on YouTube and it has become a platform for one of the biggest PR producers for companies. Various companies have proposed an optimized way of understanding and getting the opinions of the viewers from YouTube live chat and find the best possible way to provide relevant and informative content to boost the business strategy. This study uses Natural Language Processing (NLP) based approach along with NLP transformers to classify and analyses the sentiment. 2022 IEEE. -
Topic Modelling of ongoing conflict between Russia and Ukraine
Online news sites provide hotspots to extract popular ratings and opinions on a wide range of topics. Realizing what individuals are referring to and understanding their concerns and suppositions is exceptionally significant to organizations and political missions. Furthermore, it is incredibly difficult to physically peruse such enormous volumes of data and gather the themes. Keeping in mind the prevailing plight of war-Torn nations such as the recent conflict between Russia and Ukraine. This study performs aims to perform topic modelling using LDA (Latent Dirichlet Allocation) and text analysis on datasets collected from various online news websites. To increase the accuracy and efficacy of the topic modelling, a comparative analysis is proposed that elevates the performance of machine learning models. This study also develops an algorithm where the entire process can be automated from the point of data collection to finding optimum array of topics in the given dataset. Searching for insights from the collected information can therefore become very tedious and time-consuming. Topic modelling was designed as a tool to organize, search, and understand vast quantities of textual information. The topic model using LDA was utilized to do a text analysis for this research. In the beginning, researchers have scraped a total of 1178 articles that covered the war conflict between Russia and Ukraine from December 1, 2021, to May 16, 2022. After that, researcher built the LDA model and modified hyper parameters based on the coherence score Cv that was used for the model evaluation technique. When using the most effective model, prominent topics, and representative documents pertaining to each topic, topic allocation among the documents, and potential enhancements are covered in the last section. 2022 IEEE. -
Machine Learning based Food Sales Prediction using Random Forest Regression
Sales forecasting is crucial in the food industry, which experiences high levels of food sales and demand. The industry has concentrated on a well-known and established statistical model. Due to modern technologies, it has gained tremendous appeal in improving market operations and productivity. The main objective is to find the most accurate algorithms to predict food sales and which algorithm is most suitable for sales forecasting. This research work has mentioned and discussed about several research articles that revolve around the techniques usedfor sales prediction as well as finding out the advantages and disadvantages of the said techniques. Various techniques were discussed as to predicting the sales but mainly Incline Increasing Regression and Accidental Forestry Lapse is used for attention. The manufacturing has concentrated on a well-known and established statistical model. Although algorithms like Modest Direct Regression, Incline Increasing Lapse, Provision Course Lapse, Accidental Forest Lapse, Gradient Boosting Regression, and Random Forest Regression are well familiar for outdoing others, it has remained decisively established that Random Forest Regression is the most appropriate technique when associated to the others. After doing the whole examination, the Random Forest Regression technique fared well when compared to other algorithms. The feature importance is generated for the selected dataset using Python and Random Forest Regression and the nose position chart is also explainedin detail. The proposed model is compared three major parameters that are accuracy score, mean absolute error and max error. The proposed random forest regression accuracy score is improved nearly 1.83% and absolute error rate is reduced 4.66%. 2022 IEEE. -
Subscriber Preference and Content Consumption Pattern toward OTT platform: An Opinion Mining
Introduction: The outburst of the pandemic has paved the way for the immense popularity of over-The-Top (OTT) platforms among viewers. Furnishing an alternate medium to watch favorite shows and making it a new normal, the OTT platform has replaced the traditional entertainment platform. However, migrating from traditional television to an OTT platform is still a challenge in developing countries. Hence, the understanding of subscriber preferences and content consumption patterns becomes essential to planning and strategizing future business models. Purpose: The purpose of the paper is to examine the subscriber preference and content consumption pattern toward the OTT platform. In addition, this paper also investigates the popularity of leading OTT platforms among Indian viewers. Methodology: Data has been collected from the subscribers of three major OTT: Amazon Prime, Netflix Video, and Disney+Disney+Hotstar. A total of 1860 reviews were scraped as textual data and analyzed using the lexicon-based method. The polarity of the sentiments pertaining to the reviews of different platforms was analyzed using sentiment analysis. Furthermore, the topic modeling on the reviews was performed using natural language programming(NLP). Findings: The findings of sentiment analysis showed that Netflix and Disney+Disney+Hotstar had a considerable number of positive sentiments among viewers when compared to Amazon Prime Video. Eventually, the paper also showed negative sentiment towards Amazon Prime Video regarding streaming content, ad pop-ups, interface issue, shows, etc. Our findings help OTT platforms to determine which factors are driving this dramatic shift in viewer behaviour so that better strategies for attracting and retaining subscribers can be developed. Despite the rise in OTT platform popularity, this is the first study to investigate the content consumption pattern of OTT viewers comprehensively. 2022 IEEE. -
Multi-lingual Spam SMS detection using a hybrid deep learning technique
Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics. 2022 IEEE. -
An effective Approach for Pneumonia Detection using Convolution Vision Transformer
Early detection of pneumonia in patients through effective medical imaging may enable timely remedial measures and reduce the severity of the infection. There is an increase in cases among new-borns, teenagers and also people with health issues in recent years. The COVID-19 pandemic also revealed the major impact pneumonia had on the lungs and the consequences of delayed detection. The presence of the infection in the lungs is examined through images of Chest X-ray, however, for an early diagnosis of the infection, this paper proposes an automated model as a more effective alternative. Convolutional Vision Transformer (CVT) which gives an accuracy of 97.13%, and is a robust combination of Convolution and Vision Transformer (ViT), is suggested in this paper as a potential model to detect pneumonia early in patients. 2022 IEEE. -
Rebuilding the Capabilities for Post COVID-19 Pandemic: Issues and Challenges of Bangalore Model of Development
The pace of urbanization has achieved considerable momentum in recent years with 34.93 per cent of India's population living in urban areas. However, the COVID - 19 pandemic has severely affected urban development with adverse effects on people's mobility, consumption level, health and poverty. Bangalore, the capital of Karnataka and the third largest city in India, has a population of 11 million and contributes more than one third of the state's GDP. The expansion of certain sectors including Information Technology, infrastructure and spread of educational institutions has fueled Bangalore's rapid growth in the past three decades which has made it a regional superpower in India, if not South Asia. This paper explores the unique features of the 'Bangalore Model of Development' as a regional development model and provides a systematic introspection of its capabilities. It discusses the impact of the pandemic on the key driving forces of Bangalore Model and assesses the current government measures. The situation analysis with the policy prescriptions would help to strengthen and sustain the urban system during the postpandemic times. 2022 IEEE. -
ESSA Scheduling Algorithm for Optimizing Budget-Constrained Workflows
Workflows are a systematic approach for defining various scientific applications of distributed systems. They break down complicated, data-intensive processes into minor activities that can be executed serially or in parallel according to the type of application. Cloud systems need to allocate resources and schedule workflows efficiently. Despite many studies on job scheduling and resource provisioning, an efficient solution isn't found. Therefore, techniques are required to enhance resource utilization for optimal cloud computing platforms. Hence, user and provider quality of service (QoS) goals, like shortening workflows and ensuring budget limits with low energy utilization, must be considered. Enhanced Salp Swarm Optimization (ESSA) is designed to optimize makespan and QoS metrics in cloud systems. A Virtual Machine (VM's) compute capacity is related to Central Processing Unit (CPU) and memory. Size and memory demand is considered for tasks in the workflow, and task execution time is evaluated using both CPU and memory. The collated experimental outcomes convey that the newly presented technique boosts the workflows' energy utilization (up to 89%) and pushes the normalized makespan results to 3.2ms. 2022 IEEE.