Volume-12 Issue-10, September 2023 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

J1139140100925

IJITEE Journal — Fri, 19 Sep 2025 04:20:31 +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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A Revolutionary Approach to Smart Applications Using IoT: Two-Level Authentication for Automatic Safety Door and Main Door Unlocking System using IoT and Deep Learning
Rinkuben N. Patel¹, Dharmen B. Shah², Mayurdhwajsinh B. Gohil³

¹Ms. Rinkuben N. Patel, Department of Computer Science, Veer Narmad South Gujarat University, Surat (G.J), India.

²Mr. Dharmen B. Shah, Department of Computer Science, Veer Narmad South Gujarat University, Surat (G.J), India.

³Mr. Mayurdhwajsinh B. Gohil, Department of Computer Science, Veer Narmad South Gujarat University, Surat (G.J), 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: The increasing adoption of innovative door systems in residential and commercial usage has improved convenience in accessing the doors, but also introduced new security threats. Conventional approaches, such as facial recognition or pin code-based authentication, often rely on single-layer mechanisms, which can be vulnerable to intrusions. This paper proposes a two-level authentication framework to enhance door security and reliability. The primary level utilises facial recognition to grant access through a security door to registered users, including family members, authorised personnel, and trusted visitors. The second level secures the main entry door, which unlocks exclusively for recognized family members, thereby introducing an additional layer of protection. A key contribution of this approach is its ability to perform facial recognition once and leverage the verified data across both security stages, reducing redundancy while strengthening overall access control. The proposed system demonstrates the potential to improve innovative security solutions by integrating multi-layered authentication for enhanced safety in residential and office environments.

Keywords: Internet of Things, IoT, Smart Devices, Automation, Advancement, IoT Security and Risk
Scope of the Article: Computer Science and Applications

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J971509121023

IJITEE Journal — Fri, 25 Jul 2025 09:30:55 +0000

Text Independent Writer Verification using Spatial Domain Technique under Varying Writing Conditions
Sharada L. Kore¹, Shaila D. Apte²
¹Sharada L. Kore, Ph.D. Research Scholar, Bharati Vidyapeeth Deemed University College of Engineering, Pune, India.

Shaila D. Apte, Anubhuti Solutions, Pune, 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: The present work reports the results of text independent writer verification under different environment such as writing instrument, ink width, paper quality, image size, image type, scanning devices and resolution. It is observed that the style of a writer and the type of writing instrument used greatly affects the handwriting. Also, the quality of handwritten samples greatly affects the performance of writer verification system. The objective of our work is to verify a writer under the changing conditions. To improve verification rate, the directional information is captured from the image contours and the histogram of chain code is used as feature. The system is tested on our own created dataset of 261 writers with 2 samples each with different writing instruments. Total of 50% samples are used to train the system and the system is tested for remaining 50% samples. The system is tested using chain code. The total error rate (TER) is 0.76 %. The method works even under poor resolution.

Keywords: Chain Code, Handwriting Identification, Pattern Recognition, Person Authentication, Writer Verification.
Scope of the Article: Information Technology

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C981113030224

IJITEE Journal — Thu, 24 Jul 2025 12:34:12 +0000

Analyzing the Influence of E-Learning System on Student Satisfaction Through Self Determination Theory (SDT)
Damar Fatika Sati¹, Agus Efendi², Sri Sumaryati³
¹Damar Fatika Sari, S. Kom, Student of Magister Educational Technology, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.

²Dr. Agus Efendi, M. Pd, Lecturer of Educational Technology, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.

³Dr. Sri Sumaryati, M. Pd, Lecturer of Educational Technology, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.

Abstract: Research on student satisfaction on e-learning services has been widely conducted in developed countries, but only a few have conducted similar research in developing countries, including Indonesia. In Indonesia, e-learning has been applied in education, particularly at the university level. This research aims to identify the factors that influence student satisfaction in e-learning services, the role of e-learning in learning, and the supporting factors for e-learning satisfaction. E-learning has several types, but the most used in education is the Learning Management System (LMS). The research method employed is a descriptive qualitative study on LMS SPADA. Researchers used interview and observation techniques with 30 students who have used LMS for learning. The measurement of satisfaction in this study utilises Self-Determination Theory (SDT), examining three key variables: autonomy, competence, and relatedness. Furthermore, the research process on student satisfaction with e-learning has yielded positive results. Students are satisfied with the use of e-learning in their studies. The results of this study indicate that to achieve student satisfaction, all matters related to the application of e-learning need to be well prepared, especially those related to variables in SDT. The implication of this research is for all educators, students, and universities that have implemented e-learning, especially the universities studied.

Keywords: E-learning, Indonesia, Learning Management System (LMS), Satisfaction, Self Determination Theory (SDT).
Scope of the Article: Information Technology

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C979613030224

IJITEE Journal — Thu, 24 Jul 2025 12:28:14 +0000

Data Summerization and Voice Assistant
Laxman Singh¹, Ram Kumar Sharma², Nikhil Saini³, Mrtyunjy Singh⁴
¹Prof Mr. Laxman Singh, Assistant Professor, Department of Computer Science, ABES Institute of Technology, Ghaziabad (Uttar Pradesh), India.

²Ram Kumar Sharma, Student, Department of Computer Science, ABES Institute of Technology, Ghaziabad (Uttar Pradesh), India.

³Nikhil Saini, Student, Department of Computer Science, ABES Institute of Technology, Ghaziabad (Uttar Pradesh), India.

⁴Mrtyunjy Singh, Student, Department of Computer Science, ABES Institute of Technology, Ghaziabad (Uttar Pradesh), India.

Abstract: This project focuses on data collection and writing, aiming to develop a framework for efficiently summarizing extensive knowledge available on the Internet. The proposed framework leverages morphological content and semantic information to sift through the vast amount of data online. The current information landscape is overwhelming, making it challenging for individuals to quickly extract pertinent details. The sheer volume of data on the Internet poses difficulties in searching for and assimilating relevant information from diverse sources. The solution lies in the development of an automatic writing system to address these challenges effectively. Summary summarization, a crucial aspect of this framework, involves identifying and condensing the most essential and valuable information from a given dataset. The goal is to create a concise version that retains the original purpose of the data entry. The significance of this approach becomes apparent in the face of the daunting task of making sense of big data, streamlining the process and facilitating efficient extraction of meaningful insights.

Keywords: Natural Language Processing (NLP), SBERT, Transformer, Hugging face, Tokens, Machine Learning, Summarization.
Scope of the Article: Computer Science and Applications

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F95590512623

IJITEE Journal — Tue, 26 Sep 2023 09:54:26 +0000

Bangus (Chanos Chanos) Fry Counter
John C. Amar¹, Glenn J. Frencillo²
¹Dr. John C. Amar, Department of Computer Studies, University of Antique, Guintas, Hamtic, Antique, Hamtic, Antique, Philippines.

²Glenn J. Frencillo, Department of Computer Studies, University of Antique, Guintas, Hamtic, Antique, Hamtic, Antique, Philippines.

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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: Agriculture has a key role in the Philippines economy, particularly the fisheries industry, which contributes 15% of the country’s overall income. The sector offers significant employment and income opportunities for various parts of the population, contributes to export earnings, and meets a substantial portion of the population’s protein nutritional requirements. In terms of employment, the fisheries sector employs over one million people, accounting for approximately 5% of the nation’s workforce. The general objective of the study is to develop a portable bangus fry counter for the University of Antique – Hamtic Campus, Hamtic, Antique, and to create an affordable fry counter for local fish fry vendors, fish pond owners, and others who can benefit from this thesis. The researchers used the Arduino as the operating system, the light sensor for input, and the LCD for output. For data gathering, the researchers used the ISO 25010 characteristic, the weighted mean formula to calculate the mean, and the Likert scale. The prototype demonstrated excellent performance in counting the quantities of bangus fry. Based on the evaluation results, the participants strongly agreed with the device’s functionality, reliability, portability, maintainability, security, compatibility, performance efficiency, and usability.

Keywords: Fry Counter, Bangus, Chanos Chanos,
Scope of the Article: Agriculture Engineering

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J972509121023

IJITEE Journal — Tue, 26 Sep 2023 09:49:38 +0000

Deep Learning Approach for Advanced COVID-19 Analysis
Rania Alhalaseh¹, Mohammad Abbadi², Sura Kassasbeh³
¹Rania Alhalaseh, Department of Data Science, University of Mutah, Karak, Jordan.

²Mohammad Abbadi, Department of Computer Science, University of Mutah, Karak, Jordan.

³Sura Kassasbeh, Department of Computer Science, University of Mutah, Karak, Jordan.

Abstract: Since the spread of the COVID-19 pandemic, the number of patients has increased dramatically, making it difficult for medical staff, including doctors, to cover hospitals and monitor patients. Therefore, this work relies on Computerised Tomography (CT) scan images to aid in the diagnosis of COVID-19. CT scan images are used to diagnose and determine the severity of the disease. On the other hand, Deep Learning (DL) is widely used in medical research, making significant progress in medical technologies. For the diagnosis process, the Convolutional Neural Network (CNN) algorithm is used as a type of DL algorithm. Hence, this work focuses on detecting COVID-19 from CT scan images and determining the severity of the illness. The proposed model is as follows: first, classifying CT scan images into infected or not infected using one of the CNN structures, Residual Neural Networks (ResNet50); second, applying a segmentation process for the infected images to identify lungs and pneumonia using the SegNet algorithm (a CNN architecture for semantic pixel-wise segmentation) so that the disease’s severity can be determined; finally, applying linear regression to predict the disease’s severity for any new image. The proposed approach achieved an accuracy of 95.7% in the classification process, as well as segmentation rates of 98.6% for lung and 96.2% for pneumonia, respectively. Furthermore, a regression process reached an accuracy of 98.29%.

Keywords: Convolutional Neural Network (CNN), Deep Learning, Severity, Segnet, ResNet50, CT scan, VGG16.
Scope of the Article: Deep learning

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Volume-12 Issue-10, September 2023 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

J1139140100925

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