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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing
A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor. 2022 IEEE. -
Comparison between Symmetrical and Asymmetrical 13 Level MLI with Minimal Switches
Voltage source converters that are dependable and of the highest quality are offered by Multilevel Inverter to convert DC power systems to the AC power grid. One of the intriguing technologies in the field of power electronics are multilevel inverters (MLIs) in various configurations. It is also possible to integrate a few DC sources in MLIs to create a singular output, reducing the number of isolated inverters, the overall component count, and losses. MLIs are the top converters in many applications because to their capacity for medium and high-power applications. In order to produce the levels for the stair case wave shape, this research work introduces a new configuration module for asymmetrical multilevel in which capacitors are employed as DC linkages. With two unequal DC sources, the suggested Box -type modular structure will produce more voltage levels. It is useful for a variety of renewable applications since it has two back-to-back T-type inverters and minimal parts. This module contains this structured method to lessen the Total Harmonic Distortion (THD) rating and raise the quality of the sinusoidal output voltage. 2022 IEEE -
Arming Farmers with Smart Farming: The Future of Agriculture
Internet of Things (IoT) innovation is currently one of the growing fields across a diversity of industries, together with agriculture. IoT enhances our lives by making and promoting developments in a wide range of actions to encourage them to become more appropriate, practicality, and enhanced using suitable man-made recognition. Smart agricultural frameworks recognize a social trade toward more helpful, lower-cost agribusiness because of this innovation. The proposed work is to use IoT in the agriculture industry to collect real-time data (soil moisture, temperature, and so on) to help one look at a few climate scenarios from afar, efficiently, and greatly increase production. A global solution for monitoring and managing the agricultural field remotely has been proposed. Implementation of a local stand-alone field control unit that includes detection and activation capabilities. Developed a cloud solution for data storage, real-time monitoring, and historical data visualization based on the ThingSpeak cloud platform. Remote managing and control functions have been realized in both the local unit and the cloud using IoT infrastructure. 2022 IEEE. -
An Intelligent Stock Market Automation with Conversational Web Based Build Operate Transfer (BOT)
Zerodha, Upstox, Angel Broking, Groww, etc. Such companies have the most significant users of traders/investors in the equity share market. Their trust is based on their ease of use, less time-consuming process, and accurate graphs and charts of real-Time data. But what if such companies had an algorithm that could predict the future prices of any share? Not just based on historical data but also on sentimental data? This project aims to build a speech recognition chatbot like Alexa Google, which will use Recurring Neural Network-Long Short-Term Memory (RNNLSTM) and Natural Language Processing (NLP) to predict future intra-day prices. 2022 IEEE. -
Building an Industry Standard Novel Language Model Using Named Entities
In every Industry, there is a significant amount of text used in their specific domains. As these are less prevalent in the testing set, anticipating entity names in a language model is a problem faced by the entire industry. In this research a unique and very effective strategy for creating exclusionary classification models that could map entity names based on entity type information is provided. A group of benchmark datasets based on Mortgage is presented, which we used to test the below-presented model. According to experimental findings, our model achieves a perplexity level that is 64% higher than that of the most advanced language models. 2022 IEEE. -
Networks Simulation: Research Based Implementation using Tools and Approaches
The advancements in computer networks and communication technology keep network-related research in high demand. Protocols are designed to improve the environment and it is mandatory to test their effectiveness before deploying them. Deploying an untested protocol in a full-fledged real environment is not desirable as there exists uncertainty about its success. Simulation software is one of the essential tools in network research areas. It gives a platform for testing and observing newly developed protocol's behavior with less cost and risk. Different kinds of network simulators are available., some are exclusive for wired or wireless., and some are for both. There are many simulators available hence selecting the most appropriate simulation tool among them is a difficult task. This paper focuses on giving a detailed review of popular simulation tools. 2022 IEEE. -
Artificial Intelligence based System in Protein Folding using Alphafold
Artificial Intelligence has a high potential to solve many real-world problems. In the recent years researchers are dealing with one of the biggest complications in biology, which is protein folding. With the assistance of technology, we can foresee how proteins fold from a chain of amino acids into 3D shapes that do life's errands. There are mainly three big problems associatedwith folding of proteins. The first problem is there any particular folding code. The second one there is a folding system. Then the final problem is we able to determine the 3D structure of proteins. Proteins are the microscopic machines and structural building blocks of our cells. They carry out important functions like breaking down foods, storing oxygen and forming scaffolds to help cells keep their shape. Each one is built up of one amino acid chain that folds in on itself into a mostly defined structure. Each part of our body and in any other organism is made either from or by proteins and this is true for every living creature, even for viruses. The structure of very small proteins can be foreseen using the computer method. This article is all about the protein folding problem with more spotlights on the role of AI-based systems in protein structure forecasts. The motivation behind this article is to convey an overall understanding of AI-based answers for protein folding problems. 2022 IEEE -
Risk Assessment Model for Quality Management System
The ecological and economic risk assessment system and its cost were also factored into the document. The distribution of workplace challenges and hazards, represented by quantitative or subjective occupational risk metrics, was typical in the areas of building safety and environmentally responsible workers. Environmental risk assessment refers to the identification & evaluation of risks, the formulation & application of managerial decisions to lessen the chance of unfortunate conditions, and also the substantial decrease of materials or other damages. Risk assessment facilitates the transition from an area of uncertainty to one where outcomes are more or less expected. The Deming-Shewhart cycle, which would be fully linked to the policy process and performance measurement system, appears to be the implementation technique of the ecological and economic structure under consideration. It would be a cyclical sequence of the associated effective measures. A high degree of adaptability to any internally or externally stressful conditions would be ensured by the synthesis of the fundamentals of the management system & mechanisms for controlling environmental potential costs. This also guarantees the rapid identification of expert hazards, optimization and efficiency gains. 2022 IEEE. -
JRHDLSI: An Approach Towards Job Recommendation Hybridizing Deep Learning and Semantic Intelligence
The requirement of the job for people and employees for employers are al-ways in demand. This is due to the lack of proper infrastructure to reduce the unmatching job application for employers and inappropriate job recommendations for people. This chapter proposes a strategic framework with machine learning and knowledge integration to increase accuracy in the provided recommendations and increase the chance of getting a job offer. The usage of'user's search data intends job recommended more in liking of the users, and the machine learning helps in finding the accurate job recommendation. The machine learning technique used here is Radial Basis Function Neural Net-work for the classification and Knowledge Integrated using Analysis of Variance - Web Point Wise Mutual Information and Kullback Leibler (KL) divergence. All the job providers ads are retrieved from the top websites using beautiful soup. The proposed JRHDLSI architecture achieved an accuracy of 94.99% which outperformed the baseline models and was much superior. 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
Game Rules Prediction Winning Strategies Using Decision Tree Algorithms
With the availability of extensive data spanning over the years, sports have become an emerging field of research. The application of analytics in cricket has become prominent over the years. Cricket, the most loved sport in India, draws the attention of fans worldwide. The Indian Premier League is no exception. Created in 2008, this franchise-based T20 format of cricket has gripped the attention of cricket enthusiasts. With ardent fans cheering for their favorite teams, teams have mounting pressure to maintain their winning streak. One such team is the beloved Chennai Super Kings. Statistical techniques for winner prediction have become popular over the last decade. In this study, we try to frame decision rules for IPL teams to win a series using the CART algorithm. By considering Chennai Super Kings, this study aims to understand the criteria for winning and identify potential weaknesses, allowing the team to predict the likelihood of winning the IPL series. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Reconfigurable Multilevel Inverters with Minimal Switches for Battery Charging and Renewable Energy Applications
In recent years, classical inverters such as the H-bridged cascaded multilevel inverter, flying capacitor, and flying capacitor multilevel inverter have contributed in electric vehicle and non-conventional energy applications. Due to higher switching and conduction losses, as well as a greater number of power switches and driver circuits, conventional multilevel inverters do not achieve the highest performance. To obtain higher performance while reducing power losses and total harmonic distortion, individual switches are controlled by logic gates. In this proposed work, one of the inverters is considered symmetrical voltage another is asymmetrical voltage for implementing these effective topologies. The proposed single-phase seven-level voltage output and current for both symmetric and asymmetric multilevel inverters are employed to test the intended computation. The MATLAB/Simulink tool is used to implement and investigate the various parameters of proposed topologies. 2022 IEEE. -
A Study on Student Cyber Safety Consciousness in the Light of Online Learning
Our world online and networked is immersed under a wave of populism; populism spreads on the wings of internet. The recent technological advancements like the use of social media platforms and different applications made the information exchange faster and more efficient making the information access easier. To keep our information, gadgets such as cell phones, laptops, desktops, and tablets and also the internet safe, knowledge of cybersecurity is vital everywhere. In many colleges and Universities who are in to interconnected complex systems, data privacy is a huge challenge among their users. In most of the situations, due to lack of knowledge and awareness, users may engage in data breaches knowingly or unknowingly and the complete interconnected systems among the users may have a consequence of a cybercrime. This article seeks to unpack the rise of cyber-crimes and its relationship to cyber security among student groups during the pandemic where much of their interaction is online. The research aims to inquire in to the level of knowledge and awareness on cybersecurity among students during their online learning interaction using a well-structured questionnaire. The questionnaire will be focused on five parts: Awareness and Knowledge, Monitoring and Privilege, Security and Prevention, Protection from malware s and usage of removable Devices. The study is conducted using quantitative research methodology to quantitatively evaluate the knowledge of cybersecurity and inculcate an awareness against Cybercrime protection among the students. Finally, based on the analysis of collected data we present recommendations which will not forego the safety concerns for e mails, viruses, phishing, pop-up windows and forged ads which is a common problem. Some technological solutions and paths for the regulation of the cybercrimes are suggested to the respondents at the end. 2022 IEEE.