Volume-13 Issue-5, April 2024 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

J926409111022

IJITEE Journal — Mon, 29 Apr 2024 11:17:34 +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 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Missing Link Prediction in Art Knowledge Graph using Representation Learning
Swapnil S. Mahure¹, Anish R. Khobragade²

¹Swapnil S. Mahure, College of Engineering, COEP Technological University Pune (Maharashtra), India.

²Anish R. Khobragade, College of Engineering, COEP Technological University Pune (Maharashtra), India.

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© 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: Knowledge graphs are an essential and evolving field in the Artificial Intelligence domain, with multiple applications, including question answering, information retrieval, information recommendation, and Natural language processing. A knowledge graph has one significant limitation: incompleteness. This is because real-world data are dynamic and continue to evolve. The incompleteness of the Knowledge graph can be overcome or minimised by using representation learning models. Several models are classified based on translation distance, semantic information, and NN (Neural Network) models. Earlier, various embedding models were tested on mainly two well-known datasets: WN18RR and FB15k-237. In this paper, a new dataset, namely ArtGraph, has been utilised for link prediction using representation learning models to enhance the application of ArtGraph in various domains. Experimental results show that ConvKB outperformed the other models in the link prediction task.

Keywords: KG Embeddings, Artwork, Link Prediction, Neural Network
Scope of the Article: Neural Information Processing

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F985813060524

IJITEE Journal — Mon, 29 Apr 2024 11:10:57 +0000

A Framework for Innovating the Technological Startup in the Cordillera Region through an E-commerce Mobile Platform
Joan M. Peralta

Joan M. Peralta, Department of Information Technology and Computer Science, University of the Cordilleras, Baguio City, Benguet, Philippines.

Abstract: The technological startup community is beginning to be recognized in solving various needs in the Cordillera region. However, these technological startups experience limited growth due to challenges such as financial concerns, limited market reach, a lack of awareness of intellectual property rights, and inadequate digital infrastructure. Thus, the researcher proposed the development of an e-commerce mobile application that addresses the needs of startups. Through the use of data gathering techniques, the researcher determined the design considerations in terms of the architecture framework and features of the e-commerce mobile application that addressed the problems encountered by startups in the Cordillera region. The use of the 4+1 view model illustrated the architecture framework of the e-commerce mobile platform for intended stakeholders by providing a logical view, process view, physical view, development view, and scenario view. The architecture framework was used to determine the features to be integrated into the proposed e-commerce mobile application, addressing the challenges faced by technological startups in the Cordillera region.

Keywords: E-commerce, Mobile Platform, Technological Startup, 4+1 Architecture Framework
Scope of the Article: E-commerce

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E985513050424

IJITEE Journal — Mon, 29 Apr 2024 11:02:45 +0000

A Novel Cycle Leader Permutation with Elgamal Algorithm for Image Encryption
Boggana. Vandana¹, Kanusu. Srinivasa Rao², Buduri. Reddaiah³, Bodi. Susheel Kumar⁴

¹Boggana Vadana, Department of Computer Science and Technology, Yogi Vemana University, Kadapa-516005, India.

²Kanusu. Srinivasa Rao, Associate Professor, Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India.

³Buduri. Reddaiah, Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India.

⁴Bodi. Susheel Kumar, Department of Computer Science and Technology, Yogi Vemana University, Kadapa, India.

Abstract: As more people use networks in whatever capacity, security-related issues come up more frequently. These issues could be external to the network or internal to it. To address the security-related problems, the science of cryptography and network security enables the protection of resources, data quality, and network infrastructure. Firewalls and filters are utilized across many workstations to safeguard the resources. However, security services are required to protect the data during transmission to prevent unauthorized access. To guard against attacks, these services must be changed often. This paper integrates Cycle Leader permutation with Elgamal algorithms to construct such a system. These hybrid solutions can be used to prevent hackers from gaining unauthorised access to different commercial applications.

Keywords: Encryption, Decryption, Key, Cycle Leader Permutation, Elgamal.
Scope of the Article: Encryption Methods and Tools

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E984413050424

IJITEE Journal — Mon, 29 Apr 2024 10:49:40 +0000

Enhancing Arabic Sign Language Recognition using Deep Learning
Noor S. Sagheer¹, Faezah Hamad Almasoudy², Manar Hamza Bashaa³

¹Noor S. Sagheer, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.

²Faezah Hamad Almasoudy, Department of Animals Production, College of Agriculture, Kerbala University, Kerbala, Iraq.

³Manar Hamza Bashaa, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.

Abstract: In the present time, Sign language is essential for people who suffer from hearing loss or who cannot speak. Normal humans tend to overlook the significance of sign language, which is a vital means of communication for deaf and mute individuals. This study proposes a developed model for sign language recognition in Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then, set up the algorithm by developing a program using OpenCV and the Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, each containing 1,500 images that incorporated hand gestures in various environments. The dataset was divided into a training set (70%), a testing set (20%), and a validation set (10%). The results show that the suggested model achieved an accuracy rate of 94.8%, demonstrating its effectiveness and success, particularly after being tried and tested by several users and receiving their comments and feedback.

Keywords: Arabic Language, CNN Classification, Deep Learning, Image Classification.
Scope of the Article: Classification

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E984213050424

IJITEE Journal — Mon, 29 Apr 2024 10:35:19 +0000

Analysing Factors Affecting Implementation of Automated Construction Progress Monitoring in Indian Construction Industry
Mohd Sameer Malik¹, M. Kranti Kumar²

¹Mohd Sameer Malik, Post Graduate Student, Department of Architecture, School of Planning and Architecture, Vijayawada (A.P), India.

²Dr. M. Kranti Kumar, Assistant Professor, Department of Architecture, School of Planning and Architecture, Vijayawada (A.P), India.

Abstract: Automated construction progress monitoring has evolved as a critical element in modern construction projects, increasing efficiency and decision-making processes. It has gained recognition as a revolutionary technology in the global construction industry. However, its successful implementation in India presents distinct problems driven by a variety of factors, such as technological challenges, financial restrictions, a shortage of qualified labour, resistance to technology adoption, and high initial investment, among others. This research investigates the significance of automated construction progress monitoring, examines the factors that influence its adoption, and provides recommendations for its implementation in the Indian construction industry. By recognising these variables, construction stakeholders can better negotiate the hurdles and reap the benefits of automated monitoring technology.

Keywords: Automated Construction Progress Monitoring, Real-Time Monitoring, Implementation Challenges, Technology Adoption, Indian Construction Industry, Construction Project Management
Scope of the Article: Structural Engineering

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