Volume-11 Issue-1, November 2021 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

A96281111121

IJITEE Journal — Fri, 03 Dec 2021 09:57:48 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. Challenges of Industry and their Solutions, Communications #Advanced Manufacturing Technologies #Artificial Intelligence #Autonomous Robots #Augmented Reality #Big Data Analytics and Business Intelligence #Cyber Physical Systems (CPS) #Digital Clone or Simulation #Industrial Internet of Things (IIoT) #Manufacturing IoT #Plant Cyber Security #Smart Solutions – Wearable Sensors and Smart Glasses #System Integration #Small Batch Manufacturing #Visual Analytics #Virtual Reality #3D Printing # Internet of Things (IoT) #Internet of Things (IoT) & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Block Chain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for Smart City #Logic, Functional programming and Microcontrollers for IoT #Sensor Networks, Actuators for Internet of Things #Data Visualization using IoT #IoT Application and Communication Protocol #Big Data Analytics for Social Networking using IoT #IoT Applications for Smart Cities #Emulation and Simulation Methodologies for IoT #IoT Applied for Digital Contents #Microwaves and Photonics #Microwave filter #Micro Strip antenna #Microwave Link design #Microwave oscillator #Frequency selective surface #Microwave Antenna #Microwave Photonics #Radio over Fibre #Optical Communication, #Optical Oscillator #Optical Link Design #Optical Phase Lock Loop #Optical Devices #Computation Intelligence and Analytics #Soft Computing #Advance Ubiquitous Computing #Parallel Computing #Distributed Computing #Machine Learning #Information Retrieval #Expert Systems #Data Mining #Text Mining #Data Warehousing #Predictive Analysis #Data Management #Big Data Analytics #Big Data Security #Energy Harvesting and Wireless Power Transmission #Energy Harvesting and Transfer for Wireless Sensor Networks #Economics of Energy Harvesting Communications #Waveform Optimization for Wireless Power Transfer #RF Energy Harvesting #Wireless Power Transmission #Microstrip Antenna Design and Application #Wearable Textile Antenna #Luminescence #Rectenna #Advance Concept of Networking and Database #Computer Network #Mobile Adhoc Network #Image Security #Application Artificial Intelligence and Machine Learning in the Field of Network and Database #Data Analytic #High Performance Computing #Pattern Recognition #Machine Learning (ML) and Knowledge Mining (KM) #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Natural Language Processing #Probabilistic Models and Methods #Reasoning and inference #Marketing and Social Sciences #Data Mining #Knowledge Discovery #Web Mining #Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent Systems #Bioinformatics #Social Sciences #Industrial Financial and Scientific Applications of All Kind #Advanced Computer Networking Computational Intelligence #Data Management, Exploration, and Mining #Robotics #Artificial Intelligence and Machine Learning #Computer Architecture and VLSI #Computer Graphics, Simulation, and Modelling #Digital System and Logic Design #Natural Language Processing and Machine Translation #Parallel and Distributed Algorithms #Pattern Recognition and Analysis #Systems and Software Engineering #Nature Inspired Computing #Signal and Image Processing #Reconfigurable Computing #Cloud, Cluster, Grid and P2P Computing #Biomedical Computing #Advanced Bioinformatics #Green Computing #Mobile Computing #Nano Ubiquitous Computing #Context Awareness and Personalization, Autonomic and Trusted Computing #Cryptography and Applied Mathematics #Security, Trust and Privacy #Digital Rights Management #Networked-Driven Multicourse Chips #Internet Computing #Agricultural Informatics and Communication #Community Information Systems #Computational Economics, Digital Photogrammetric #Remote Sensing, GIS and GPS #Disaster Management #e-governance, e-Commerce, e-business, e-Learning #Forest Genomics and Informatics #Healthcare Informatics #Information Ecology and Knowledge Management #Irrigation Informatics #Neuro-Informatics #Open Source: Challenges and opportunities #Web-Based Learning: Innovation and Challenges #Soft computing Signal and Speech Processing #Natural Language Processing #Communications #Microstrip Antenna #Microwave #Radar and Satellite #Smart Antenna #MIMO Antenna #Wireless Communication #RFID Network and Applications #5G Communication #6G Communication #Algorithms and Complexity #Sequential, Parallel and Distributed Algorithms and Data Structures #Approximation and Randomized Algorithms #Graph Algorithms and Graph Drawing #On-Line and Streaming Algorithms #Analysis of Algorithms and Computational Complexity #Algorithm Engineering #Web Algorithms #Exact and Parameterized Computation #Algorithmic Game Theory #Computational Biology #Foundations of Communication Networks #Computational Geometry #Discrete Optimization #Software Engineering and Knowledge Engineering #Software Engineering Methodologies #Agent-Based Software Engineering #Artificial Intelligence Approaches to Software Engineering #Component-Based Software Engineering #Embedded and Ubiquitous Software Engineering #Aspect-Based Software Engineering #Empirical Software Engineering #Search-Based Software Engineering #Automated Software Design and Synthesis #Computer-Supported Cooperative Work #Automated Software Specification #Reverse Engineering #Software Engineering Techniques and Production Perspectives #Requirements Engineering #Software Analysis, Design and Modelling #Software Maintenance and Evolution #Software Engineering Tools and Environments #Software Engineering Decision Support #Software Design Patterns #Software Product Lines #Process and Workflow Management #Reflection and Metadata Approaches #Program Understanding and System Maintenance #Software Domain Modelling and Analysis #Software Economics #Multimedia and Hypermedia Software Engineering #Software Engineering Case Study and Experience Reports #Enterprise Software, Middleware, and Tools #Artificial Intelligent Methods, Models, Techniques #Artificial Life and Societies #Swarm Intelligence #Smart Spaces #Autonomic Computing and Agent-Based Systems #Autonomic Computing #Adaptive Systems #Agent Architectures, Ontologies, Languages and Protocols #Multi-Agent Systems #Agent-Based Learning and Knowledge Discovery #Interface Agents #Agent-Based Auctions and Marketplaces #Secure Mobile and Multi-Agent Systems #Mobile Agents #SOA and Service-Oriented Systems #Service-Centric Software Engineering #Service Oriented Requirements Engineering #Service Oriented Architectures #Middleware for Service Based Systems #Service Discovery and Composition #Service Level Agreements (Drafting, Negotiation, Monitoring and Management) #Runtime Service Management #Semantic Web #Data Modelling, Mining and Data Analytics #Aggregation, Integration, and Transformation #Web and Text Mining #Data Mining Methods, Techniques, and Tools #Data Analytics Modelling and Algorithms #Patterns and Frameworks #Data Visualization #Knowledge Systems and Engineering #Knowledge Acquisition #Knowledge-Based and Expert Systems #Knowledge Representation and Retrievals #Knowledge Engineering Tools and Techniques #Time and Knowledge Management Tools #Knowledge Modelling, Integration, Transformation, and Management, #Uncertainty Knowledge Management #Knowledge Visualization #Mobile Computing and Mobile System Engineering #Mobile App Design and Development #Innovative Mobile Applications #Pervasive Computing #Mobile System Validation and Test Automation #Software & System Quality of Service #Soft Computing #Software and System Testing Methods #Quality Assurance Process, Standards, and Systems #Software Safety Systems #Software Test Automation and Tools #Software Dependability, Reliability, Scalability #Software & System Security #Software and System Security and Privacy #Mobile App Security and Privacy #Encryption Methods and Tools #Security Service Systems #Cloud, Sensor Cloud and Mobile Cloud Security #Mobile System Integrity, Security and Fault Tolerance #Emergent Topics #Cloud Computing and Innovative Cloud-Based Application Systems #Mobile Cloud Computing and Application Services #Big Data Analytics Application Systems #Innovative Sensing Cloud and Systems #Large-Scale Cyber Systems #IoT and Smart City Application Systems #Big Data Quality Validation #Big Data Application Quality Services #Smart Learning and Innovative Education Systems #Learning Software Design Engineering #Smart Learning Methods and Environments #Online Learning Systems #Mobile Enabled Learning Systems and Tools #Computer Networks and Inventive Communication Technologies #Addressing & Location Management #Broadband Access Technologies #Adhoc and Sensor Networks #Cross Layer Design and Optimization #Heterogeneous Wireless Networks #High Speed Networks #Internet and Web Applications #Measurement & Performance Analysis #Mobile & Broadband Wireless Internet #Mobile Networks & Wireless LAN #Network Architectures #Network Based Applications #Network Protocols & Wireless Networks #Network Operations & Management #Network Security Trust, & Privacy #Next Generation Internet & Web Architectures #Peer to Peer and Overlay Networks #QOS And Resource Management #Recent Trends & Developments in Computer Networks #Routing, Switching and Addressing Techniques #Self-Organizing Networks and Networked Systems #Ubiquitous Networks #Virtual & Overlay Networks #Wireless Communications #Wireless Mesh Networks and Protocols #Wireless Multimedia Systems #Personal and Wearable Networks #Wireless ad hoc & Sensor Networks #Information-Centric Networking #Embedded Networks #Opportunistic Networking #Delay-Tolerant Networks #Cognitive Radio Networks #Vehicular Networks #Smart Grid Communications #Underwater Sensor Networks #Cyber-Physical Systems #Social Networks #Mobile and Ubiquitous Networking #Green Networking #Overlay and Peer-to-Peer Networks #Local-Area and Metropolitan-Area Networks #Storage-Area Networks #Routing and Transport Protocols #Big Data Networking #Cloud Computing and Networking #Software Defined Networking and Network Function Virtualization #Internet of Things #Link Technologies #Adaptive Networking Applications #Authentication, Authorization, Accounting #Security and Privacy #Cross-Layer Optimization #Mobility and Location-Dependent Services #Multimedia and Real-Time Communication #Machine-to-Machine Communications for Smart Environments #Smart Cities #Network Traffic Characterization and Measurements #Network Management, Reliability and QoS #Performance Evaluation of Networks #Testbeds for Network Experiments #Network Coding #Optical and High-Speed Access Networks #Resent Engineering and Nano Technology #Advanced Computing Architectures and New Programming Models #Bio-Science and Bio-Technology #Collaborative Applications #Communication Architectures for Pervasive Computing #Computer Vision #Computer Science and Its Applications #Computational Techniques in Civil Engineering #Control and Automation #Concrete Engineering #CAD/CAM/CIM #CAE #Database Theory and Application #Data Base Management System #Data Mining #Decision Making #Digital Signal Processing Theory #Distributed Sensor Networks #Distributed Computing #Mechanical Design #Evolutionary Computing and Intelligent Systems #Expert Approaches #Environmental Engineering #Fuzzy logics #Foundation Engineering #FEA #GPS and Location-Based Applications #Grid Networking/ Computing #GEOTECH Engineering #Highways #Human Computer Interaction (HCI) #Image Analysis and Processing #Information and Data Security #Internet Technologies, Infrastructure, Services & Applications #Nano Technology #Mobile Computing and Applications #Multimedia Communications #Network Modelling and Simulation #Network Performance, Protocols, Sensors #Networking Theory and Technologies #Open Models and Architectures #Pattern Recognition #Perception and Semantic Interpretation #Production #Real-Time Information Systems #Remote Sensing #Security Technology and Information Assurance #Soft Computing #Software Engineering & Its Applications #Signal Control System & Processing #Speech Interface, Speech processing #Structural Engineering #Thermal Engineering #Ubiquitous Multimedia Computing #Vision-Based Applications #VLSI Algorithms #Web Technologies #Textile Engineering #Recent on Mechanical Engineering and Technology #Fluid Mechanics #Applied Mathematics and Mechanics #Biomechanics #Heat Transfer #Solid Mechanics #Refrigeration and Air Conditioning #Renewable Energy Technology #Materials Engineering #Composite Materials #Marine Engineering #Petroleum and Mineral Resources Engineering #Textile Engineering #Industrial Engineering #Operational Research #Manufacturing Processes #Machine Design #Quality Control #Mechanical Maintenance #Tribology #CAD/CAM #Aeronautical Engineering #Production Engineering #Welding Technology #Robotics Engineering #Aerospace Engineering #Metallurgy #Rock Mechanics and Mining Sciences #Solid and Structural Mechanics #Theoretical and Applied Fracture Mechanics #Advance Civil Engineering and Technology #Assemblage and System #Behaviour of Structures #Behaviour of Structures under Seismic Loads #Building and Environmental Acoustics #Building Climate Systems #Building Energy #Civil and Environmental Engineering #Coastal Engineering #Composite Materials #Concrete Structures #Construction Economics #Construction Engineering #Design and Performance of Green Building #Design Optimization of Structures #Earthquake Engineering #Energy Efficient Building Technology #Energy Saving Building Materials #Evaluation of Building Envelope Systems under Structural and Environmental Loads #Evaluation of Glazing Systems for Energy Performance #Fire Engineering #Foundations Dynamics #Geotechnical Engineering #Health Monitoring and Life Prediction of Structures #High Performance Concrete #Hydraulic Engineering #Life Cycle Engineering #Materials and Durability #Materials Engineering #Mechanics and Materials Aspects of Advanced Construction Materials #Municipal or Urban Engineering #Nondestructive Testing and Evaluation #Numerical Modelling of Structures #Optimal Design of Structures #Properties and Mechanics of Concrete #Residential, Commercial, Industrial and Public Works #Seismic Evaluation of Building Nonstructural Components #Simulation Optimization and Risk Management #Soil-Structure Interaction #Structural Engineering #Structural Evaluation of Panelized and Masonry Wall Systems #Structural Reliability Analysis #Surveying #Sustainable Structures #Transportation Engineering #Ventilation and Indoor Air Quality #Water Supply and Drainage #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Optimal Neuro Fuzzy Classification for Arrhythmia Data Driven System
Hela Lassoued¹, Raouf Ketata², Hajer Ben Mahmoud³
¹Hela Lassoued*, National Institute of Applied Science and Technology INSAT, Tunis, Tunisia.
²Raouf Ketata, National Institute of Applied Science and Technology INSAT, Tunis, Tunisia.
³Hajer Ben Mahmoud, National Institute of Applied Science and Technology INSAT, Tunis, Tunisia.
Manuscript received on November 23, 2021. | Revised Manuscript received on November 28, 2021. | Manuscript published on November 30, 2021. | PP: 70-80 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A96281111121 | DOI: 10.35940/ijitee.A9628.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS configuration needs mainly the choice of an initial Fuzzy Inference System (FIS) and the training algorithm. Indeed, two clustering algorithms which are the fuzzy c-means (FCM) and the subtractive (SUBCLUST) algorithms, are applied to generate the initial FIS. Besides, for tuning the ANFIS membership function and rule base parameters, Gradient descent and evolutionary training algorithms are also evaluated. Gradient descent consists of the backpropagation (BP) method and its hybridization with the least square algorithm (Hybrid). However, the evolutionary training methods involve the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Therefore, eight (8) ANFIS are configured and assessed. Accordingly, a comparison study between their obtained Root Mean Square Error (RMSE) is analyzed. At the end, we have selected an optimal ANFIS which uses the SUBTRUCT algorithm to generate the initial FIS and the GA to tune its parameters. Moreover, to guarantee the effectiveness of this work, a comparison study with related works is done.
Keywords: Data driven system, classification, arrhythmias Neuro-Fuzzy, gradient descent, evolutionary algorithms, optimization.
Scope of the Article: Classification

Download PDF

The post A96281111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

B82971210220

IJITEE Journal — Fri, 03 Dec 2021 09:51:16 +0000

PM_2.5 Concentration Prediction By Data Mining Method
Hung Thuan Nguyen¹, Chi Quynh Nguyen²
¹Hung Thuan Nguyen, Department of Bachelor of Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
²Chi Quynh Nguyen*, Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
Manuscript received on November 29, 2021. | Revised Manuscript received on November 27, 2021. | Manuscript published on November 30, 2021. | PP: 64-69 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.B82971210220 | DOI: 10.35940/ijitee.B8297.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The global air pollution is constantly increasing and causing negative effects on human health such as respiratory, cardiovascular diseases and cancers. Recently, pollution in Hanoi has become increasingly worse, especially when PM2.5 concentration is always at high level. Thus, PM2.5 prediction is of more urgency to issue early forecasts. Depending on air data including meteorological indicators and air pollution indicators collected in Hanoi, we have proposed a new characteristic extraction method that gave better results when uing the same algorithm compared to those of old methods. XG Boost algorithm was applied to predict the concentration of PM2.5 and the test showed that the accuracy of this algorithm is higher than that of other data mining algorithms while the training time is significantly lower.
Keywords: air quality forecasting, data mining, PM2.5 prediction, XG Boost.
Scope of the Article: Data Mining

Download PDF

The post B82971210220 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A95901111121

IJITEE Journal — Fri, 26 Nov 2021 06:01:14 +0000

Visualization of Control Processes and Code Validation
Monika Rybczak¹, Natalia Wenta², Dawid Trzciński³
¹Monika Rybczak*, Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland.
²Dawid Trzcińśki, Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland.
³Natalia Wenta, Department of Ship Automation, Gdynia Maritime University, Gdynia, Poland.
Manuscript received on November 20, 2021. | Revised Manuscript received on November 25, 2021. | Manuscript published on November 30, 2021. | PP: 59-63 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95901111121 | DOI: 10.35940/ijitee.A9590.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The article contains an overview of articles related to the description of control process visualization. It provides short information on how to visualize the production line based on two programming environments: Factory IO and Inventor together with Matlab/Simulink. The analysis of these two environments concerns control of a virtual 3D object from a real PLC. Both virtual production line projects are based on control from the S7-1214 DC/DC/DC controller. Currently, there is a need to validate the program code or control process which has been done using several commercially available programs.
Keywords: Validate Process Technology, S7-1200, Factory Io, Visualization of The Control Process.
Scope of the Article: Knowledge Visualization

Download PDF

The post A95901111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A96031111121

IJITEE Journal — Thu, 25 Nov 2021 11:00:27 +0000

Influence of Online Shopping Buying Behaviour in Post Covid 19
P. Mari Selvam¹, A. Gomathi²
¹Dr. P. Mari Selvam*, Assistant Professor, Department of Commerce, CMS College of Science and Commerce (Autonomous), Coimbatore (Tamil Nadu), India.
²Dr. A. Gomathi, Assistant Professor & Head, Department of Psychology, CMS College of Science and Commerce (Autonomous), Coimbatore (Tamil Nadu), India.
Manuscript received on November 15, 2021. | Revised Manuscript received on November 23, 2021. | Manuscript published on November 30, 2021. | PP: 56-58 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A96031111121 | DOI: 10.35940/ijitee.A9603.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The corona virus which causes a highly infectious of Corona virus disease (COVID-19) that has affected more than 4 lakh people in around the world. Since it has been increased during the pandemic period online shopping usage, rural, urban and globally. In the current scenario many youngster’s changing the attitude has purchased to online shopping because social distancing and self-quarantine efforts. Hence the online shopping promoters like Amazon, flip kart, Reliance digital and other agencies are for the time being too given the importance its available fulfilment and logistics facility to serve the basic needs such as household products, packaged food, health care, hygiene, personal safety and other high priority products. It is for the time being going to taking orders for lower-priority to high priority products. In this study to analyze the impact of online buying behaviour increased in after pandemic period.
Keywords: Buying Behaviour, Covid-19, Online Shopping, Online Shopping Features.
Scope of the Article: e-Commerce

Download PDF

The post A96031111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A96241111121

IJITEE Journal — Thu, 25 Nov 2021 10:51:40 +0000

A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process
Maria Mohammad Yousef
Maria Mohammad Yousef*, Department of Computer Science, Al al-Bayt University, Jordan
Manuscript received on November 13, 2021. | Revised Manuscript received on November 20, 2021. | Manuscript published on November 30, 2021. | PP: 50-55 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A96241111121 | DOI: 10.35940/ijitee.A9624.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.
Keywords: Dimensionality Problem, Feature Selection, classification, Genetic Algorithm.
Scope of the Article: Classification

Download PDF

The post A96241111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A95991111121

IJITEE Journal — Thu, 25 Nov 2021 10:42:10 +0000

VisQuelle: Visual Question based Elementary Learning Companion – A System to Facilitate Learning Word Object Associations
Sandhya Vidyashankar¹, Rakshit Vahi², Yash Karkhanis³, Gowri Srinivasa⁴
¹Sandhya Vidyashankar*, PES Center for Pattern Recognition, PES University, Bengaluru, India and Department of Mechanical Engineering, SSN College of Engineering, Chennai (Tamil Nadu), India.
²Rakshit Vahi, Department of Computer Science and Engineering, PES University, Bengaluru (Karnataka), India.
³Yash Karkhanis, Department of Computer Science and Engineering, PES University, Bengaluru (Karnataka), India.
⁴Gowri Srinivasa, Department of Computer Science and Engineering, PES University, Bengaluru (Karnataka), India.
Manuscript received on November 15, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 41-49 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95991111121 | DOI: 10.35940/ijitee.A9599.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: We present an automated, visual question answering based companion – Vis Quelle – to facilitate elementary learning of word-object associations. In particular, we attempt to harness the power of machine learning models for object recognition and the understanding of combined processing of images and text data from visual-question answering to provide variety and nuance in the images associated with letters or words presented to the elementary learner. We incorporate elements such as gamification to motivate the learner by recording scores, errors, etc., to track the learner’s progress. Translation is also provided to reinforce word-object associations in the user’s native tongue, if the learner is using Vis Quelle to learn a second language.
Keywords: Visual question answering; object recognition; question generation; question answering; word-object association.
Scope of the Article: Visual Analytics

Download PDF

The post A95991111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A95981111121

IJITEE Journal — Thu, 25 Nov 2021 10:35:41 +0000

Augmented Reality based Mobile Application for Energy Monitoring and IoT Device Control
Abishek R¹, D. Vaishali², Adhitya Narayan R³, Vignesh Sundar M⁴
¹Abishek R*, B.Tech. Student, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai (Tamil Nadu), India.
²Dr. D. Vaishali, Assistant Professor, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai (Tamil Nadu), India.
³Adhitya Narayan R, B.Tech. Student, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai (Tamil Nadu), India.
⁴Vignesh Sundar M, B.Tech. Student, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai (Tamil Nadu), India.
Manuscript received on November 13, 2021. | Revised Manuscript received on November 20, 2021. | Manuscript published on November 30, 2021. | PP: 35-40 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95981111121 | DOI: 10.35940/ijitee.A9598.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: IoT has become an integrated part of our lives changing ways in which we operate our everyday appliances. In addition to making our home appliances smart, it has become a common trend for companies to adopt industry 4.0, which uses various sensors to monitor the equipment, machinery, and the work environment. We often come across multiple brands which make smart appliances but each brand comes with its separate mobile application for the appliance’s operation. This requires us to switch between Apps to control these appliances if we at all remember which App controls which appliance. We intend to solve these two major inconveniences by creating a single mobile application that can control all these appliances using Augmented Reality technology. All we have to do is point our camera at the appliance that we need to operate and the App will display control options in real-time AR. This paper produces five important contributions:
1) An AR-based mobile application to control IoT devices and monitor the environment.
2) Implementing the mobile application using Unity 3D engine and Vuforia SDK.
3) Integrating a commercially available IoT device with the mobile application.
4) Integrating custom-made hardware IoT device with mobile application.
5) Integrating this combination to make our industries and homes smarter
Keywords: Augmented reality, Vuforia SDK, unity engine, AR with IOT, Home automation, Internet of things
Scope of the Article: IoT

Download PDF

The post A95981111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A95941111121

IJITEE Journal — Thu, 18 Nov 2021 08:43:47 +0000

Pigeon Metheuristic Optimized Generative Adversarial Networks and ARKFCM Algorithms for retinal Vessel Segmentation and Classification
R. Kiran Kumar¹, K. Arunabhaskar², Ch. Mani Mala³
¹Dr. R. Kiran Kumar, Department of Computer Science & Engineering, Krishna University, Machilipatnam (AP), India.
²K. Arunabhaskar*, Department of Information Technology, Aditya Engineering College, Surampalem (AP), India.
³Dr. CH. Mani Mala, MBBS, DO, FCO (LVPEI), Senior Consultant & Phaco Surgeon, Vasan Eye Care, Kakinada (AP), India.
Manuscript received on November 10, 2021. | Revised Manuscript received on November 15, 2021. | Manuscript published on November 30, 2021. | PP: 28-34 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95941111121 | DOI: 10.35940/ijitee.A9594.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Automatic evaluation of retinal vessels acts a significant part in diagnosis of several ocular and systemic diseases. Eye diseases must be diagnosed early to avoid severe infection and vision loss. The method of segmentation and classification of the retinal blood vessel identification is most difficult tasks in computerized fundus imaging now a days. To solve this problem in this paper, to locate retinal vessel in the retinal vessel, Adaptive Regularized Kernel Based Fuzzy Clustering Means (ARKFCM) algorithm-based segmentation is used. For retinal vessel prediction purpose in this paper a PIGEON optimization-based learning rate modified Generative Adversarial Networks (GAN) algorithm is introduced. Additionally, to improve the proposed classification performance input image is transformed with the aid of Discrete Wavelet Transform (DWT). The DWT applied Low Low (LL) image and segmented images are cascaded. The cascade images are used for training and testing. The proposed system has validated with the help of DRIVE and STARE Publically available datasets. They are studied by applying a Convolutional Neural Network, an instantly trained neural network for predicting retinal vessel. In the end, the system is checked for system efficiency using the results of modeling based on MATLAB. The scheme guarantees an accuracy of 92.77% on DRIVE dataset and 98.85% on STARE dataset with a minimum average classification error of 2.57%. Further, we recommended to physician for implement the real time clinical application; this scheme is highly beneficial for doctors for identifying retinal blood vessels.
Keywords: Discrete Wavelet Transform (DWT), Adaptive Regularized Kernel Based Fuzzy Clustering Means (ARKFCM), Diabetic Retinopathy (DR), and modified Generative Adversarial Networks (GAN) algorithm.
Scope of the Article: Classification

Download PDF

The post A95941111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A95851111121

IJITEE Journal — Fri, 12 Nov 2021 11:38:29 +0000

Performance of Building with Basements Under Seismic Excitation Considering Soil Structure Interaction
Abdullah Lala¹, Toshif Patel², Ravi Karkar³, Jigar Sevalia⁴
¹Abdullah Lala*, Department of Civil Engineering, Sarvajanik College of Engineering and Technology Surat, India.
²Toshif Patel, Engineer, Varjlal V. Ambaliya Structural Engineer and Consultant, Surat, India.
³Ravi Karkar, Engineer, Varjlal V. Ambaliya Structural Engineer and Consultant, Surat, India.
⁴Dr. Jigar Sevalia, Professor & Head, Department of Civil Engineering, Sarvajanik college of Engineering and Technology Surat, India.
Manuscript received on January 10, 2021. | Revised Manuscript received on January 20, 2021. | Manuscript published on January 30, 2021. | PP: 7-27 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95851111121 | DOI: 10.35940/ijitee.A9585.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In modern construction, there is a trend to go deeper below the grade level in terms of basements which can be utilized for parking, shopping malls or a combination of both. In such cases, dynamic soil properties have a significant effect of activating dynamic soil structure interaction phenomenon during earthquake. Here in present study an effort is made to study the behavior of a building by varying five and three number of basements considering dynamic soil structure interaction. Issues like influence zone to be considered for dynamic soil structure interaction, behavior of building with basements under different water level conditions for two different types of layered soil and their comparison with fixed based structure for a real-life structure is dealt with. It is observed that dynamic soil structure interaction can significantly change the behavior and also the failure pattern of the building and hence it is recommended to perform dynamic soil structure interaction for building with multiple basements.
Keywords: Basements, Dynamic soil structure interaction, layered soil, multiple underground stories, Nonlinear direct integration time history.
Scope of the Article: Soil Structure

Download PDF

The post A95851111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).

A95891111121

IJITEE Journal — Fri, 12 Nov 2021 11:30:34 +0000

Deep-COVID-19: Deep Learning for COVID 19 Detection from X-ray Images
Ahmed Hashem El Fiky
Ahmed Hashem El Fiky*, Department of Systems and Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.

Manuscript received on November 06, 2021. | Revised Manuscript received on November 10, 2021. | Manuscript published on November 30, 2021. | PP: 1-6 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A95891111121 | DOI: 10.35940/ijitee.A9589.1111121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy.
Keywords: Coronavirus; Convolution Neural Networks; Deep-Learning; Covid-19.
Scope of the Article: Deep Learning

Download PDF

The post A95891111121 appeared first on International Journal of Innovative Technology and Exploring Engineering (IJITEE).