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Internet of Things Enhancing Sustainability of Business
When one assumes that the current era is the era for digital revolution then the Internet of Things (IoT) is supposed to be one of the most significant among all. It is the IoT which is assisting the bussinesses. Current IoT applications, on the other hand, are still in their early stages, and the true capacity of viable business opportunities has yet to be realised. However, IoT adoption may need considerable integration and experienced personnel. It also frequently generates new requirements in terms of security and interoperability, or the ability for different computer hardware systems as well as software applications to "speak"to one another. 2022 IEEE. -
Internet of Things and Machine Learning based Intelligent Irrigation System for Agriculture
Irrigated agriculture methods need a significant volume of water, and causes water waste. It is critically necessary to install an efficient watering system and lessen the volume of water wasted on this tiresome chore. It is a huge benefit of the computer vision (ML) - the Internet of Everything (Ot) era to construct expert machines that carry out this work successfully with little human endeavour. This work suggests an Embedded device Fluid ounces suggestion method for efficient water use with little farm involvement. In the agricultural field, IoT sensors are set up to capture important atmospheric and surface data. The obtained information is sent to and stored on a cloud-based server, where machine learning techniques are used to evaluate the information and recommend treatment to the farmers. This recommender system has an internal development process that makes the solution resilient and flexible. The test demonstrates that the suggested method operates admirably on the agricultural dataset from the National Institutes of Technology (Kit) Bhubaneswar as well as the information that we obtained. 2022 IEEE. -
Cloud Computing, Machine Learning, and Secure Data Sharing enabled through Blockchain
Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. 2022 IEEE. -
Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach
Crude oil price shocks have a significant impact on aggregate macroeconomic indices like GDP, interest rates, investment, inflation, unemployment, and currency rates, according to empirical evidence. Various factors like GDP, CPI, and Gold prices show a considerable impact on the Crude old prices. The correlation analysis between these variables can help the machine learning model to find the highly impacting factor of the target variable. The advanced machine learning algorithms can be used to find the most relevant variable impacting the crude oil price followed by predicting the crude oil price. Time series analysis algorithms can forecast the crude oil prices for the specific period ahead. In the current study it was observed that US dollar and CPI show a high impact on Crude oil prices. The study has implemented six machine learning algorithms out of which the ARIMAX was found to be the most efficient model. VAR and ARIMA models are used to successfully forecast the crude oil prices for the next 5 years. From the current research, a machine learning model has been obtained as an outcome of the study, which will help economists in the future to understand the dynamics of crude oil prices driver and forecast it for the near future. 2022 IEEE. -
Airline Twitter Sentiment Classification using Deep Learning Fusion
Since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. Blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. Businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LSTM architecture for sentiment analysis. For comparison of the proposed system, LSTM alone also tested for similar dataset. LSTM was given an accuracy of 91% and the proposed ensemble framework with LSTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques. 2022 IEEE. -
Hotel Recommendation System Based on Customer's Reviews Content Based Filtering Approach
Recommendation systems are fantastic tools for remembering people's ideas in order to gain knowledge more efficiently and selectively. Recently, booking and searching for hotels online has become more common. As it takes more time, online hotel research is growing more quickly. In addition, the amount of knowledge accessible online is continuously expanding. User preferences have a big impact on hotel recommendations. The most effective recommendations may be made by recommendation systems by utilising historical user preference data. To solve this problem, recommender systems have suggested content-based filtering methods. Product recommendations, recommendations for websites, news articles, restaurants, and TV series are all examples of applications for content-based recommender systems. The dataset for this project includes client evaluations of the offered Kaggle profile. Word embedding, word2vec, and TF-IDF natural language processing methods were used for feature extraction. The algorithm shows the user the top 10 suggested hotels based on the user's past knowledge of the hotel's location. 2022 IEEE. -
A Comparative Analysis On Machine Learning Algorithm for Score Prediction and Proposal of Enhanced Nae Bayes
Sports attracted a lot of people to watch various games all over the world. India is not an exception. Among various games, cricket has special attention. Cricket in India contributes to the Indian economy on a large scale. Cricket is also known for the broad amount of data gathered for each team, season, and player. Hence, cricket is a perfect domain to work on various data analysis and machine learning approaches to acquire useful insights and information. In this paper, algorithms were used to enhance the output of the team in a sports league, particularly, IPL (cricket). It reflects the performance of the team on a deeper analysis of the requirements of T20 cricket. 2022 IEEE. -
Smart Embedded Framework of Real-Time Pollution Monitoring and Alert System
The sustainability and progress of humanity depend on a clean, pollution-free environment, which is essential for good health and hygiene. Huge indoor auditorium does not have proper ventilation for air flow so when the auditorium is crowded the carbon di-oxide is emitted and it stays there for many days this may be a chance to spreading of COVID-19 and other infectious diseases. Without proper ventilation virus may present in the indoor auditorium. In the proposed system, emissions are detected by air, noise, and dust sensors. If the signal limit is exceeded, a warning is given to the authorities via an Android application and WiFi, and data is stored in cloud networks. In this active system, CO2 sensor, noise sensor, dust sensor, Microcontroller and an exhaust fan are used. This ESP-32 based system is developed in Arduino Integrated Development Environment (Aurdino IDE) to monitor air, dust and noise pollution in an indoor auditorium to prevent unwanted health problems related to noise and dust. More importantly, using IoT Android Application is developed in Embedded C, which continuously records the variation in levels of 3 parameters mentioned above in cloud and display in Android screen. Also, it sends an alert message to the users if the level of parameters exceeds the minimum and maximum threshold values with more accuracy and sensitivity. Accuracy and sensitivity of this products are noted which is very high for various input values. 2022 IEEE. -
Rubitics: The Smarter GCMS for Mars
A GCMS stands for a Gas Chromatograph and Mass Spectrometer. These two instruments are used to identify compounds from both soil and atmospheric samples. The GCMS usually has a mass of around 40 kilograms and is the size of a microwave oven, but what if we could downsize it? Downsizing the GCMS means that the number of equipment and instruments that can be used and carried by a rover can drastically increase. Rubitics is essentially a GCMS, only smaller and more efficient. This paper discusses the way Rubitics functions and how a GCMS can be remodeled and used to its fullest potential. The column of the Gas Chromatograph is replaced with composite materials to increase the flexibility of the tube, thereby increasing the number of columns along with finger-like projections on the interiors, which will aid in a much more precise separation of compounds. The inert carrier gas container is changed with a more durable, strong composite that will be instrumental in reducing the mass of the cylinder, and a safer chemically unreactive material will ensure complete pure storage. Rubitics will also contain a cooling system so as to be more power-efficient and aid in obtaining precise results. The material of the oven used in the gas chromatograph will be of much more insulating capacity (thermal resistance), lighter in mass, and smaller in size. Rubitics maintains the optimum shape to provide the most temperature and energy-efficient GCMS ever. Rubitics houses a compact electronic bay with sensors and a microprocessor for analysing the different components. The detectors' values are processed in the onboard microprocessor with the help of TinyML. This light algorithm can help in reducing the bandwidth consumed in transmitting unnecessary data to the ground station through providing in-situ data filtration. The paper also contemplates using such an algorithm to improve the efficiency of GCMS. In conclusion, Rubitics will be the future of GCMS technologies and sample analysis on different planetary terrains. Due to its re-engineered structure, it occupies lesser weight, size, and space. Rubitics thereby changes the number and quality of experiments that can be performed on Mars, leading to better insights for successful future habitation. Copyright 2022 by Ms. Harshini K Balaji. Published by the IAF, with permission and released to the IAF to publish in all forms. -
Modern Technology Usage for Education Field during COVID-19: Statistical Analysis
The COVID-19 pandemic has had vast effects on the concept of education as a whole. During the pandemic, students had no access to physical teaching practices, which had been adapted worldwide as the principal way of education since the 1800's. Due to the restrictions imposed to garner safety from the spread of the virus, this methodology had to be modified based on the situation at hand. Alternatives through the usage of Virtual Learning Platforms (VLP), Online Tutoring Platforms (OTP), Web Conferencing Platforms (WCP) and multiple assessment tools like plagiarism checker, poll sites, quiz platforms, online proctored examinations (OPE) started gaining popularity among all institutes to cope with the limitations levied. The technologies molded a path for student-teacher interaction, performance assessments, document sharing and online tutoring. This research highlights the lack of online tutoring equipment, educators' limited expertise with online learning, the knowledge gap, a inimical atmosphere for independent study, equity, and academic success in postsecondary learning. The goal of this review is to present an overview of available technologies for online teaching that can be used to improve the quality of education during COVID-19. 2022 IEEE. -
Detection of Malicious Nodes in Flying Ad-hoc Network with Supervised Machine Learning
An Ad-hoc network (FANET) is a new upcoming technology which has been used in several sectors. Ad-hoc networks are mostly wireless local area networks (LANs). The devices communicate with each other directly instead of relying on a base station or access points as in wireless LANs for data transfer. In an Ad-hoc network the communication between one node to another in a FANET is not secured and there isn't any authorized protocol for secured communication. Therefore, we suggest an algorithm to detect the malicious node in a network. This algorithm uses Linear regression to calculate the reputation or trust value of a node in the network. Then the above found trust value is used to classify the node as normal node or malicious node based on the Logistic Regression Classification. Thus, allowing a secure communication of data and avoiding attacks. 2022 IEEE. -
Optical Character Recognition system with Projection Profile based segmentation and Deep Learning Techniques
Optical character recognition is the solution to convert text from printed or scanned documents into editable data. This project is aimed at building a Optical character recognition system that recognizes digital text. A document is first detected using contour-based detection technique without altering the angle of the image and is segmented into lines, once the lines are segmented the words embedded in them are extracted. This segmentation is done using projection profiling method. Characters are then segmented words with vertical projection profiling from the extracted words. These characters are fed into an image recognition model for recognition. The recognition model is CNN based deep learning model. Modified VGG16 architecture is used here to extract maximum features from the images and then classify them. To train the model a dataset is created from a repository of digital character dataset. The dataset consists of images of 153 font variants. 2022 IEEE. -
A study of Autoregressive Model Using Time Series Analysis through Python
A Time-series investigation is a simple technique for dividing information from reconsideration perceptions on a solitary unit or individual at ordinary stretches over countless perceptions. Timeseries examination can be considered to be the model of longitudinal plans. The most widely used method is focused on a class of Auto-Regressive Moving Average (ARMA) models. ARMA models could examine various examination questions, including fundamental cycle analysis, intercession analysis, and long-term therapy impact analysis. The model ID process, the meanings of essential concepts, and the factual assessment of boundaries are all depicted as specialized components of ARMA models. To explain the models, Multiunit time-series plans, multivariate time-series analysis, the consideration of variables, and the study of examples of intra-individual contrasts across time are all ongoing improvements to ARMA demonstrating techniques. [1] 2022 IEEE. -
State-of-art Techniques for Classification of Breast Cancer: A Review
Cancer is an unexpected and unclear disease that puts many people at risk. Breast cancer has surpassed prostate cancer as the most common cancer in women, as well as the main cause of cancer-related mortality in women. Breast cancer rates have been rising in India for several years, with 100,000 new cases recorded each year. In India, there are up to one million breast cancer patients at any given moment. The survival rate of breast cancer has increased in recent years as a result of advances in technology, effective treatment, and medical care delivery. It extends the lives of the sufferers and improves their quality of life. Breast cancer can be detected using a variety of imaging methods. Radiologists can utilize a computer-aided diagnostic technique to discover and diagnose irregularities earlier and more quickly. Many Computer-Aided Diagnosis methods have been developed to identify breast cancer in its early stages using mammography images. The computer aided diagnostics systems mostly focus on identifying and detecting breast nodules. Staging breast cancer at its detection needs to be focused on, as the treatment is based on the stage of cancer. As a result, this study focuses on producing evaluations on computer aided diagnostics approaches for segmenting nodules and identifying different stages of breast cancer, thereby assisting radiologists in assessing the illness. 2022 IEEE. -
Revolution of the Indian Agricultural Landscape using Machine Learning and Big Data Techniques: A Systematic Review
The world of Big Data has been rapidly expanding into the domains of Engineering and Machine Learning. The biggest challenge in the Big Data landscape is the incompetence of processing vast amounts of data in a time-efficient manner. The agriculture domain has so long only relied on traditional method for yield prediction. This can be bettered by using novel Machine Learning techniques and innovative thinking. The study provides the review of most of the techniques already implemented in the ML, Big Data and Agriculture domain- traditional and modern- while focusing on highlighting the difference in accuracy between the traditional methods and the more advanced methods. 2022 IEEE. -
Design of Reconfigurable Filter Structure Based on FRM for Wideband Channelizer?
A reconfigurable FIR bank of filters are essential for digital channelizer in wideband system. FRM is a extensively used method to generate a sharp transition width sub-bands or channels for digital channelizer. The aim of this work is to design multiple non-uniform sharp transition width FIR bank of filters with reduced number of multipliers and group delay for wideband channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on FRM filters and exponential modulation (EM) technique. The performance of the proposed structure is illustrated with the help of an example. Result shows that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE. -
Influence of heat treatment on the tensile and hardness characteristics of friction stir weld joints of dissimilar aluminium alloys
Friction stir welding (FSW) is a solid-state low energy input welding technique. Most capable of joining very high strength alloys, which are finding wide range of applications in automobile and aerospace components. The current research focuses on the influence of post weld heat treatment on mechanical properties of friction stir weld joints of AA 7075 and AA 5052 dissimilar aluminum alloys. The trial experiments have been carried out using design of experiments (L16 Orthogonal Array) and the optimized process parameters have been selected based on the maximum hardness and the corresponding ultimate tensile strength (UTS). Further, the friction stir welding is accomplished with optimized process parameters (L9 Experimental trial) viz., the feed rate of 100?mm/min, tool rotational speed of 1200?rpm, tool offset of (-) 0.5?mm and using a cylindrical taper pin tool profile. The post heat treatment has been carried out on the friction stir weld joints obtained using the optimized parameters and the mechanical properties of the L9 Heat Treated (L9 - HT) and L9 - Non Heat Treated (L9 - NHT) specimens have been compared. The results shows that the post heat treated weld joints have higher micro hardness and tensile strength compared to the non-heat-treated weld joints. This is majorly attributed to recrystallization and elimination of voids due to the change in the microstructure of the weld joint. 2022 Author(s). -
Modelling and CFD simulation of vortex bladeless wind turbine
When the forces act on a bluff body in the wind flow direction, vortices are formed. Vortex bladeless wind turbine oscillates as a result of the vortices generated due to VIV. When the vortex shedding frequency is nearer to the natural frequency of the structure, maximum amplitude of vibration occurs and coincidentally power is generated. 3D models are designed to stimulate flow at a Reynolds number of 50000. This paper focuses on modelling the bladeless wind turbine based on semi-vortex angle and also 1) to study the vortices pattern and vorticity of different models 2) to study the drag and lift coefficients. In this paper vortex turbine is designed with certain parameters of dimension in Solid Edge and CFD analysis is carried out in Simscale software. Different model performance parameters like power, natural frequency and coefficient of power are compared among different models to opt for the best vortex bladeless wind turbine design. 2022 Author(s). -
The design and analysis of helical cross - Axis wind turbine
Environmental conditions such as high turbulence, low wind speed, and persistent changes in oncoming wind direction can minimize the performance of a horizontal axis wind turbine (HAWT). Some specific vertical axis wind turbine (VAWT) designs can work fine in these rare functioning conditions but still, they pose an occasional power coefficient. So a unique design of a helical cross-axis wind turbine (HCAWT) was modeled which will operate under multiple wind directions such as horizontal wind stream and vertical wind stream from the underside of the turbine. The HCAWT consists of three helical vertical blades and six horizontal blades arranged in cross-axis orientation for enhancing its performance and self-starting behavior. The obtained analysis study results show that the power generated by the HCWAT was improved when compared to the Straight-Bladed VAWT. Both the turbines were placed at height of 100, 150, 200 & 250?mm in the simulation study, coefficient of power (Cp) achieved by HCAWT was 0.43, 0.52, 0.48, and 0.51 at an RPM of 554, 512, 474 and 449 respectively whereas in the case of Straight-Bladed VAWT was 0.15, 0.18, 0.13 and 0.23 at an RPM of 179, 189, 212 and 233 were obtained. 2022 Author(s). -
INDIVIDUAL AND GROUP VARIATIONS IN WAYFINDING AMONG USERS IN AN EDUCATIONAL BUILDING
The effective performance of users in an Educational Building is determined by the available resources and also the environment in which they dwell. Wayfinding is a daily occurrence for every user of an academic institution and this is facilitated through the distinct articulation of different spaces and recognizable circulation systems. The user behavior in a known/unknown building varies as an individual and with a group of individuals. This variation can be observed in an enclosed space and public setting. For an individual, the psychological state could influence navigating within the building whereas, for a group of individuals, the group dynamics could influence each other to navigate. The paper uses mixed methods to understand and assess the individual and group variations in wayfinding. The study was undertaken in a recently constructed School of Architecture at CHRIST University, Bengaluru. The understanding was accomplished with elaborate literature studies and the assessment was through the field observation techniques and surveys carried out with identified users like frequent individuals, new individuals, frequent groups, and new groups.The study tells that for both individuals and groups, the parameters like architectural elements, sensorial qualities, wayfinding behavior, gender, and psychological state influence them in wayfinding. It was also noted that most of the student users prefer shortcuts rather than the formal entance and lobby to navigate the classrooms. Accomplishing easy, comfortable, and efficient wayfinding within an educational building requires effective layout planning. These findings aim to contribute to the detailed understanding of effective layout planning in an educational building and its impact on user behavior for architects and decision-makers. ZEMCH Network.