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Smart Facial Emotion Recognition with Gender and Age Factor Estimation
Human-Computer Interaction (HCI) in an intelligent way, which aims at creating scalable and flexible solutions. Big tech firms and businesses believe in the success of HCI as it allows them to profit from on-demand technology and infrastructure for information-centric applications without having to use public clouds. Because of its capacity to imitate human coding abilities, facial expression recognition and software-based facial expression identification systems are crucial. This paper proposes a system of recognizing the emotional condition of humans, given a facial expression, and conveys two methods of predicting the age and gender factors from human faces. This research also aims in understanding the influences posed by gender and age of humans on their facial expressions. The model can currently detect 7 emotions based on the facial data of a person - (Anger, Disgust, Happy, Fear, Sad, Surprise, and Neutral state). The proposed system is divided into three segments: a.) Gender Detection b.) Age Detection c.) Emotion Recognition. The initial model is created using 2 algorithms - KNN, and SVM. We have also utilized the architectures of some of the deep learning models such as CNN and VGG - 16 pre-trained models (Transfer Learning). The evaluation metrics show the model performance regarding the accuracy of the Recognition system. Future enhancements of this work can include the deployment of the DL and ML model onto an android or a wearable device such as a smartphone or a watch for a real-time use case. 2022 Elsevier B.V.. All rights reserved. -
Solar PV Tree Designed Smart Irrigation to Survive the Agriculture in Effective Methodology
The global economy benefits significantly from agriculture. However, there are significant issues and difficulties in the irrigation sector as a result of a significant regional imbalance in power supply, water availability, rainfall, and adoption of technology. The most economical approach to supporting agriculture in the modern day is through irrigation powered by renewable energy. Productivity is impacted by environmental issues, defective irrigation systems, and unknowable soil moisture content in agricultural fields. Traditional watering systems might lose up to 50% of the water used due to ineffective irrigation, evaporation, and overwatering. As a result, the proposed study will modify solar tree-based smart irrigation systems that use the most recent sensors for real-Time or old data to influence watering flows and change watering schedules to enhance the system efficiency. One application of a wireless sensor network is proposed for low-cost wireless controlled irrigation and real-Time monitoring of soil water levels using Arduino controllers. Data is gathered for drip irrigation control using wireless acquisition stations powered by renewable energy, which lowers the risk of electrocution and boosts output. 2022 IEEE. -
Data Reduction Techniques in Wireless Sensor Networks with AI
Due to their numerous uses in practically every part of life and their related problems, such as energy saving, a longer life cycle, and better resource usage, the research of wireless sensor networks is ongoing. Its extensive use successfully saves and processes a considerable volume of sensor data. Since the sensor nodes are frequently placed in challenging locations where less expensive resources are required for data collection and processing, this presents a new difficulty. One method for minimizing the quantity of sensor data is data reduction. A review of data reduction methods has been provided in this publication. The different data reduction approaches that have been put forth over the years have been examined, along with their advantages and disadvantages, ways in which they can be helpful, and whether or not using them in contexts with limited resources is worthwhile. 2022 IEEE. -
Review On Image based Coffee Bean Quality Classification: Machine Learning Approach
Specialty coffee's demand is growing worldwide as coffee drinkers continue to look for the freshest and highest-quality flavors. Depending upon the quality, there are two categories in the coffee industry, that is specialty coffee and commodity/commercial coffee. Coffee beans are graded via visual inspection and cupping. A 300g sample of green coffee beans is used for visual assessment, and faulty beans are counted. As per the 'Specialty Coffee Association of America' (SCAA), defect can be either primary or secondary. For a coffee to be a specialty, it should have less than 5 secondary defects and zero primary defects. In this survey we have presented the coffee bean quality-related research which includes various machine learning approaches in classifying the coffee beans. The study has achieved quite promising prediction accuracies and was evaluated with test data. We have done a study on coffee bean quality classification and are willing to contribute an arabica coffee bean dataset and detection of coffee bean quality using transfer learning with higher accuracy. 2022 IEEE. -
Mapping the Field of Research; Computational Intelligence and Innovation
This paper measures and maps the past studies in the field of Computational Intelligence and Innovation and further understand the application of Computational Intelligence in the field of study of innovation related to businesses. The bibliometric analysis shows the associations of various sub themes of research that was done between the period 2000 to Aug 2022. Scopus database is used to collect relevant documents of the field of study where 115 documents are sourced. The descriptive nature of the field of studies is analyzed in detail and further using VOS Viewer, the network analysis study is conducted to understand the association of authors, author country publication, themes and publication pattern, in detail. Further, an in-depth review analysis is done to understand the application of Computational Intelligence in the fields of Business Management and Social Science with aids innovation in the respective fields. Recent studies focus on machine learning, neural network, digital transformation, internet of things and other upcoming areas. The growth in these sub themes exhibit the multidisciplinary research happening in this field. This is paving way for future researchers to use the already found computing intelligence techniques to varied subject areas like medicine, management, economics etc., to foster innovation. 2022 IEEE. -
Image Steganography Using Discrete Wavelet Transform and Convolutional NeuralNetwork
The practice of steganography involves concealing messages within another thing, which is referred to as a carrier. Is thus performed in order to build up a covert communication channel in a rather way that any observers whom has access to such a channel will not be able to detect the act of communication itself. In this research, using the process of stenography, a secret text is transferred across a communication channel using an image as a cover. Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) is used in the above process. The encoding and decoding operation is done by using DWT while the preprocessing and training of images is done by CNN. The training and prediction rate of CNN is 72.4 %. 2022 IEEE. -
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.