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Application of LSTM Model for Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE. -
Image Processing and Artificial Intelligence for Precision Agriculture
Precision agriculture is a novel approach to increase the productivity of crops that employs recent technologies such as Artificial Intelligence, WSN, cloud computing, Machine Learning, and IoT. This paper reviews the development of different techniques effectively used in precision agriculture. The paper details the technological impact on precision agriculture followed by the different image processing schemes such as Satellite imagery and unmanned aerial vehicle (UAV). The role of precision agriculture is disease detection, weed detection from UAV images, and detection of trees and contaminated soils from satellite imagery is discussed. It reviews the impact of artificial intelligence (AI) namely machine learning &deep learning in precision agriculture. The performance of the recent image processing schemes in precision agriculture is analyzed. The paper also discusses the challenges that exist in implementing the precision agriculture system. 2022 IEEE. -
Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning
Heart disease is the leading cause of mortality globally according to the World Health Organization. Every year, it results in millions of mortalities and thus billions of dollars in economic damage throughout the world. Many lives can be saved if the disease is detected early and accurately. The typical methods to predict or diagnosis heart diseases require medical expertise. Such facilities and experts are relatively expensive and not very commonly available in under developed and developing countries. Recent times, much research is done on leveraging technology for the prediction as well as diagnosis of heart diseases. Machine Learning techniques have been extensively deployed as quick, inexpensive, and noninvasive ways for heart disease identification. In this work, we present a machine learning approach in detecting heart disease using a dataset that contains vital body parameters. We used seven different models and combined them with Soft-Voting and Hard-Voting ensemble approaches to improve accuracy in 7-model and various 5-model combinations. The ensemble combinations of 5 models achieved the highest test accuracy score of 94.2%. 2022 IEEE. -
Gesture based Real-Time Sign Language Recognition System
Real-Time Sign Language Recognition (RTSLG) can help people express clearer thoughts, speak in shorter sentences, and be more expressive to use declarative language. Hand gestures provide a wealth of information that persons with disabilities can use to communicate in a fundamental way and to complement communication for others. Since the hand gesture information is based on movement sequences, accurately detecting hand gestures in real-time is difficult. Hearing-impaired persons have difficulty interacting with others, resulting in a communication gap. The only way for them to communicate their ideas and feelings is to use hand signals, which are not understood by many people. As a result, in recent days, the hand gesture detection system has gained prominence. In this paper, the proposed design is of a deep learning model using Python, TensorFlow, OpenCV and Histogram Equalization that can be accessed from the web browser. The proposed RTSLG system uses image detection, computer vision, and neural network methodologies i.e. Convolution Neural Network to recognise the characteristics of the hand in video filmed by a web camera. To enhance the details of the images, an image processing technique called Histogram Equalization is performed. The accuracy obtained by the proposed system is 87.8%. Once the gesture is recognized and text output is displayed, the proposed RTSLG system makes use of gTTS (Google Text-to-Speech) library in order to convert the displayed text to audio for assisting the communication of speech and hearing-impaired person. 2022 IEEE. -
Micro grid Communication Technologies: An Overview
Micro grid is a small-scale power supply network designed to provide electricity to small community with integrated renewable energy sources. A micro grid can be integrated to the utility grid. Due to lack of computerized analysis, mechanical switches causing slow response time, poor visibility and situational awareness blackouts are caused due to cascading of faults. This paper presents a brief survey on communication technologies used in smart grid and its extension to micro grid. By integration of communication network, device control, information collection and remote management an intelligent power management system can be achieved 2022 IEEE. -
Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks
A low computational complexity FIR bank of filters are essential for spectrum sensing in wireless networks. FRM is a widely used method to generate a sharp transition width sub-bands or channels. The intention of this work is to design multiple non-uniform sharp transition width FIR bank of filter with low computational complexity for spectrum sensing in cognitive radio for IoT networks. The design parameters of the proposed structure are calculated in an efficient way. The proposed structure is designed based on the FRM filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is illustrated with the help of an example. Result indicates that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE. -
Digitalization of Online Classes Among Higher Secondary Students in the Emerging Shift of Post Covid-19 (Second Wave)
The second wave of COVID-19 in India has left higher secondary school students befuddled, unhappy, and unsure about their future. During the second wave of the COVID-19 epidemic, a number of factors influence the effectiveness of online learning. Hence, the main objective of this research paper is focused on understanding the factors influencing online learning among higher secondary students. Researchers identified variables such as attitude, tools and technology, and quality of teaching and social support through extensive literature review. The research study adopted snowball sampling technique and used a survey-based online questionnaire for collecting the data; responses were obtained from 394 respondents from the state of Kerala in India. PLS-SEM was used to test the proposed hypotheses. The results of the study indicate that quality of teaching is the only factor that impacts the effectiveness of online classes among higher secondary students. Attitude, technology and tools, and social support are observed to have insignificant impact on online learning effectiveness. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Breast Cancer Survival Prediction using Gene Expression Data
Breast cancer is one of the most common forms of cancer in the world.[1]. Breast, skin, colon, pancreatic, and other 100 types of cancer have founded globally. An accurate breast cancer prognosis can save many patients from having unnecessary treatment and the huge medical costs that come with it. Multiple gene mutations can possibly transform a normal cell into a cancerous one. Genomic variations and traits have a significant effect on cancer. Genetic abnormalities caused by various circumstances drive numerous efforts to find biomarkers of breast cancer advancement. Early Detection of Cancer types is the only way to recover the patients from this acute disease. In this paper, a proposed Deep learning algorithm and Machine learning algorithms are used to predict the survival of cancer patients using clinical data and gene expression data. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset is split into clinical and gene data for detailed preprocessing. This proposed method gives a better understanding of the condition and assesses how effective treatment methods are by using Deep Learning and Machine Learning models on gene data. Logistic Regression is the most accurate method identified. Grenze Scientific Society, 2022. -
A Reliable Method of Predicting Water Quality Using Supervised Machine Learning Model
Water contributes to around 70% of the world's exterior and is perhaps the primary source essential to supporting life. The rapid growth of urban and industrial geographies has prompted a disintegration of the quality of water at a concerning pace, bringing about nerve-racking sicknesses. Water quality has been expectedly assessed through costly and tedious lab and measurable examinations, which render the contemporary thought of continuous observing disputable. The disturbing results of helpless water quality require an elective strategy, which is speedier and more economical. With this inspiration, this exploration investigates a progression of administered AI calculations to appraise the Water Quality Index (WQI), which acts as a unique attribute to express the generic nature of water. The proposed system utilizes multiple info boundaries, specifically, temperature, pH, dissolved O2 concentration, and all-out broken down molecules. Of the multitude of utilized regression calculations and slope boosting, the water quality index can be expected most productively, with an MSE of 0.27. The propositioned study accomplishes acceptable precision by utilizing a minimum number of features to improve the chances of it getting implemented progressively in water quality recognition frameworks. 2022 IEEE. -
A review on the scope of using calcium fluoride as a multiphase coating and reinforcement material for wear resistant applications
Solid lubricants play a vital role in the smooth and safe operation of many tribological industrial applications like cutting and forming tools, rolling and sliding contact bearings, gears, cams and protective coating in gas turbine engines for aerospace applications. Generally liquid lubricants are widely used for reducing the friction between the contacting parts which reduce the wear rate and increase the life of the parts. However, these liquid lubricants become useless when they are exposed to high temperature, high pressure and vacuum environmental conditions. Solid lubricants are those materials that can suitably reduce the friction and wear between the contacting or sliding surfaces that are in extreme environments like low and high temperature and pressure. Among the different types of solid lubricants, calcium fluoride is widely used owing to its excellent lubricity at elevated temperature. This paper initially describes the criteria for selecting solid lubricant and provides a comprehensive summary on calcium fluoride solid lubricant which can be used as a coating material in various high temperature metal and ceramic matrix composites for wear resistant applications. Further, investigations related to the selection of optimized coating parameters, synerging multiphase solid lubricants and soft metals with optimal percentage, selection of filler materials, mismatch in coefficient of thermal expansion and its impact on coating life are summarised and discussed. Finally, the scope of synthesizing calcium fluoride solid lubricant from discarded eggshell powders is explored. 2022 Elsevier Ltd. All rights reserved. -
Design of a Decision Making Model for Integrating Dark Data from Hybrid Sectors
The research on Dark data, from its definition to identification and utilization is a widely identified and encountered research problem since 2012 when Gartner defined Dark data as every possible information that an organization collects, process, analyze and store throughout regular business activities, but usually fails to make use of the stored information for other suitable purposes. The presence of dark data and its impact has been experienced by every sector, these data occupy large storage and remain unused. In this paper, we analyze Dark Data and proposed a design model to utilize dark data from multiple sectors and providing a solution to any critical situation a person might be in. For eg: Multiple cash transactions from an organizational bank account in a hospital successively over a period of 2-3 days may indicate a health emergency of any particular employee from that organization. Thus we are considering institutional data, medical data, and banking data in which machine learning algorithms can contribute huge changes in the current system and can help the decision-makers to make better decisions. The paper also proposes a few techniques and methods for the conversion of unstructured dark data to structured one and some extraction techniques for data using NLP and Machine Learning. Grenze Scientific Society, 2022. -
Review of open space rules and regulations and identification of specificities for plot-level open spaces to facilitate sustainable development: An Indian case
Rapid urbanization and an increase in the alteration of natural resources have led to climate crises, driving the need to promote sustainable development. Urban open space management plays a vital role in such scenarios. Research on urban open spaces has been mainly conducted at regional, municipal, and neighborhood scales. Rarely has the focus been on the plot-level potentials and management of open spaces. Therefore, the study looks into the Indian development control rules and regulations and identifies that although these stipulate the percentage of open space for development on each plot, specificities for open spaces are unclear. Further, the study analyses quantitative and qualitative aspects of open spaces for selected group housing schemes in Pune city. The inquiry shows that per capita open space in Pune is comparatively lower than national standards. The quantitative aspects include FSI, building ground coverage, built-up area, number of floors, and number of dwelling units, and each relates to open spaces in one way or another. The qualitative interpretations disclose that a plot-level open space can significantly impact the regional-level open space network. Hence, the research advocates a bottom-up approach wherein plot-level open space can become the focus in formulating new norms and policies for sustainable development. Published under licence by IOP Publishing Ltd. -
Smart Attendance Management System using IoT
Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking 'absent(A)' or 'present(P)' on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call's, we have proposed a biometric student attendance system based on IoT. The proposed system records students' attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student's attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students' attendance. 2022 IEEE. -
Internet of Things and Cloud Computing Involvement Microsoft Azure Platform
The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (Io T) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service's architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure Io Tservice, mainly Io THub and Io TCentral, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability. 2022 IEEE. -
Smart Online Oxygen Supply Management though Internet of Things (IoT)
We are surrounded by oxygen in the air we We cannot even exist without the ability to breathe. The need for oxygen has increased during the COVID19 pandemic, and although there is enough oxygen in our country, the main issue is getting it to hospitals or those in need on time. This is simply due to a significant communication gap between suppliers and hospitals, so we plan to implement an idea that will close this gap using real-time tracking as we can track the movement of oxygen tankers by gathering the requirements. We are using an ESP32 Wi-Fi module, a MEMS pressure sensor that enables the combination of precise sensors, potential processing, and wireless communication, such as Wi-Fi, Bluetooth, IFTTT, and MQTT protocols, to implement it successfully. The pressure sensor publishes the value of oxygen remaining from the location to the MQTT broker. 2022 IEEE. -
Firefly Algorithm andDeep Neural Network Approach forIntrusion Detection
Metaheuristic optimization has grown in popularity as a way for solving complex issues that are difficult to solve using traditional methods. With fast growth of the available storage space and processing capabilities of the modern computers, the machine learning domain, that can be succinctly formulated as the process of enabling the computers to make successful forecasts based on the previous experiences, has recently been under spectacular growth. This paper presents intrusion detection approach by utilizing hybrid method between firefly algorithm and deep neural network. The basic firefly algorithm, as a frequently employed swarm intelligence method, has several known deficiencies, and to overcome them, an enhanced firefly algorithm was proposed and used in this manuscript. For experimental purposes, KDD Cup 99 and NSL-KDD datasets from Kaggle and UCL repositories were taken and comparison with other frameworks that have been validated for the same datasets was executed. Based on simulation data, proposed method was able to establish better values for accuracy, precision, recall, F-score, sensitivity and specificity metrics than other approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Analysis of Machine Learning and Deep Learning to Predict Breast Cancer
According to the report published by American Cancer Society, breast cancer is currently the most prevalent cancer in women. In addition, it is the second leading cause of death. It needs to be taken into serious consideration. Earlier and faster detection can help in the earlier and easier cure. Normally, medical practitioners take a large amount of time to understand and identify the presence of cancer cells in the human body. This can lead to serious complications even to the death of the individual. Hence there is a need to identify and detect the presence of this disease very accurately and in a shorter span of time. Like every other industry, the medical industry is shifting its paradigm to automation giving excellent results having high accuracy and efficiency, which is achieved using Artificial Intelligence. There are two sets of models developed based on the numerical dataset Wisconsin and image dataset BreakHis. Machine Learning algorithms and Deep Learning algorithms were applied on the Wisconsin dataset. Meanwhile, Deep Learning models were used for analysis of the Breakhis dataset. Machine Learning models- Logistic Regression, K Neighbors, Naive Bayes, Decision tree, Random Forest and Support vector classifiers were used. Deep Learning models- normal deep learning models, Convolutional Neural Network (CNN), VGG16 & VGG19 models. All the models have provided a very good accuracy ranging between 75% and 100%. Since medical research has a requirement for higher accuracy, these models can be considered and embedded into several applications. Grenze Scientific Society, 2022. -
Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes
Microservices and Kafka have become a perfect match for enabling the Event-driven Architecture and this encourages microservices integration with various opensource platforms in the world of Cloud Native applications. Kubernetes is an opensource container orchestration platform, that can enable high availability, and scalability for Kafkacentric microservices. Kubernetes supports diverse autoscaling mechanisms like Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA) and Cluster Autoscaler (CA). Among others, HPA automatically scales the number of pods based on the default Resource Metrics, which includes CPU and memory usage. With Prometheus integration, custom metrics for an application can be monitored. In a Kafkacentric microservices, processing time and speed depends on the number of messages published. There is a need for auto scaling policy which can be based on the number of messages processed. This paper proposes a new autoscaling policy, which scales Kafka-centric microservices deployed in an eventdriven deployment architecture, using a Reinforcement Learning model. 2022 IEEE. -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Analysis of error rate for various attributes to obtain the optimal decision tree
The competitiveness and computational intelligence are required to increase the gross profit of the product in a market. The classification algorithm rpart is applied on retail market dataset. The regression rpart decision tree algorithm is implemented with principal component analysis to impute data in the missing part of the dataset. The objective is to obtain an optimal tree by analysing cross validation error, standard deviation error, and number of splits and relative error of various attributes. The results of various attributes by ANOVA method are compared to choose the best optimal tree. The tree with minimum error rate is considered for the optimal tree. Copyright 2022 Inderscience Enterprises Ltd.