Volume-14 Issue-7, June 2025 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

G110214070625

IJITEE Journal — Wed, 04 Jun 2025 10:32:38 +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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Decoding Consumer Sentiment through Machine Learning: Analysing Social Media Trends and Behaviours
C.K. Kotravel Bharathi¹, K. Elakkiyan²

¹Dr. C.K. Kotravel Bharathi, Former Dean, SRM University – Tiruchirappalli Campus, Tamil Nadu, India.

²K. Elakkiyan, Final year B.Tech. (IT) Student, Vellalar College of Engineering and Technology, Erode, Tamil Nadu, India.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© 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: Social media platforms have become indispensable channels for public opinion, customer feedback, and brand perception. Analysing this vast repository of user-generated content enables businesses to gain deep insights into consumer sentiment and behaviour. This paper presents the “Social Media Sentiment Analyzer,” an interdisciplinary initiative that combines marketing and Information Technology to develop a machine learning-based tool for sentiment analysis. The tool processes social media posts to classify them as positive, negative, or neutral, offering organizations actionable insights for strategic decision-making.

Keywords: Consumer Sentiment, Machine Learning, NLP, Marketing, Behaviour Insights.
Scope of the Article: Data Analytics

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F109714060525

IJITEE Journal — Wed, 04 Jun 2025 10:25:28 +0000

RiskBERT: A Pre-Trained Insurance-Based Language Model for Text Classification
Rida Ghafoor Hussain

Rida Ghafoor Hussain, Researcher, Department of Information Engineering, University of Florence, Siena Italy.

Abstract: The rapid growth of insurance-related documents has increased the need for efficient and accurate text classification techniques. Advances in natural language processing (NLP) and deep learning have enabled the extraction of valuable insights from textual data, particularly in specialised domains such as insurance, legal, and scientific documents. While Bidirectional Encoder Representations from Transformers (BERT) models have demonstrated state-of-theart performance across various NLP tasks, their application to domain-specific corpora often results in suboptimal accuracy due to linguistic and contextual differences. In this study, I propose RiskBERT, a domain-specific language representation model pre-trained on insurance corpora. I further pre-trained LegalBERT on insurance-specific datasets to enhance its understanding of insurance-related texts. The resulting model, RiskBERT, was then evaluated on downstream clause and provision classification tasks using two benchmark datasets – LEDGAR [1] and Unfair ToS [2]. I conducted a comparative analysis against BERT-Base and LegalBERT to assess the impact of domain-specific pre-training on classification performance. The findings demonstrate that pre-training on insurance-specific corpora significantly improves the model’s ability to analyse complex insurance texts. The experimental results show that RiskBERT significantly outperforms LegalBERT and BERT-Base, achieving superior accuracy in classifying complex insurance texts, with 96.8% and 92.1% accuracy, respectively, on the LEDGAR and Unfair ToS datasets. These findings highlight the effectiveness of domain-adaptive pre-training and underscore the importance of specialised language models for enhancing insurance document processing, making RiskBERT a valuable tool for this purpose.

Keywords: Clause, Domain-Specific, Insurance, Legal, Pre-Training,
Scope of the Article: Information Technology

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F109514060525

IJITEE Journal — Wed, 04 Jun 2025 10:17:04 +0000

A Novel PSO-Gain Super-Twisting Integral Sliding Mode Controller for Sensor-Less Speed Control of Saturated DFIM
PABAME Frédéric¹, NFAH MBAKA Eustace²

¹PABAME Frederic, Department of Physics, Faculty of Sciences, University of Dschang,, Cameroon.

²NFAH MBAKA Eustace, Department of Physics, University of Dschang, IUT Fotso Victor, Bandjoun, Cameroon.

Abstract: This work deals with the performance and optimisation of the speed control of a doubly fed asynchronous machine (DFIM), modelled considering the magnetic circuit’s saturation. The speed control strategy of the machine, thus implemented, is a super twisting algorithm with integral action, consisting of five regulators: four for stator and rotor currents, and one for speed regulation. The control was achieved without a mechanical sensor, thanks to an observer that reconstructs the machine’s rotation speed from the readings of the currents and voltages it absorbs. The coefficients of the regulators and those of the observer were determined by the Particle Swarm Optimisation (PSO) methods. Thus, the system’s efficiency was tested and validated across several speed ranges, including very low speeds, low speeds, and high speeds, using the MATLAB/Simulink simulation software. The control system’s performance was demonstrated by varying the set speed according to a predefined profile. The resistive torque and the rotor and stator resistances values were modified by up to 50% to demonstrate the insensitivity to disturbances or variations in the machine parameters.

Keywords: DFIM, Saturated Magnetic Circuit, PSO, Sliding Mode, Super-Twisting, Integral Action, Observer.
Scope of the Article: Control and Automation

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F109414060525

IJITEE Journal — Wed, 04 Jun 2025 10:09:08 +0000

Effect of Typha Australis Aggregate on the Mechanical Strength of Concrete
Brahim TCHOU¹, Abdallah DADI MAHAMAT², Malloum Soultan³, Abakar ALI⁴, Salif GAYE⁵

¹Brahim TCHOU, Researcher, Department of Electrical, National Higher Institute of Science and Technology of Abeche, Ouaddai, Chad.

²Abdallah DADI MAHAMAT, Researcher, Department of Energy Engineering, National Higher Institute of Science and Technology of Abeche, Chad.

³Mallaoum Soultan, Researcher, Department of Mechanical Engineering, University of N’Djamena, N’djamena Chad.

⁴Abakar Ali, Researcher, Department of Civil Engineering, National School of Public Works, N’djamena, Chad.

⁵Salif Gaye, Professor, Department of Mechanical Engineering, IUT of the University of Thies, Senegal.

Abstract: As part of this work, different dosages of Typha Australis aggregate, mixed with fine sand from the Chari River in the city of N’Djamena and cement, were used to formulate samples of Typha concrete that we characterized with a view to its use in construction. With this in mind, we carried out a particle size analysis of the sand. This allowed us to notice that the soil is purely sandy and has good characteristics for manufacturing concrete. The proportions of Typha used fluctuate between 0.5% and 5% in steps of 0.5. On the other hand, we produced sixty-six (66) prismatic samples (one for each scenario), measuring 4x4x16 cm, with Typha percentages ranging from 0.5% to 5% in increments of 0.5%, and control samples with no Typha additives for each scenario. We carried out two analysis scenarios. In the first scenario, S1, the ratio of cement to sand is 1/4, while in the second scenario, S2, the ratio of cement to sand is 1/3. The study focuses on the curing processes after 7 and 28 days. The results obtained showed that the Typha concrete studied possesses the required mechanical characteristics, making it a practical material for use in partitions and low-load-bearing structures with low heights. However, increasing the proportion of Typha decreases the mechanical characteristics, rendering the concrete less suitable for its intended purpose. The flexural test shows a strength of 1.05 MPa for the S1 series on the 7th day and 1.28 MPa for the S2 series on the 28th day. The compressive test indicates strength of 0.32 MPa for the S1 series on the 7th day and 0.33 MPa for the S2 series on the 28th day. The results indicate that incorporating Typha into the concrete develops a material suitable for use as wall infill.

Keywords: Typha Australis; Cement; Chari River Sand; Mechanical Characterization.
Scope of the Article: Mechanical Engineering and Applications

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E466114050625

IJITEE Journal — Wed, 04 Jun 2025 09:56:02 +0000

IoT-Based Electricity Theft Detection System
Priyanka Ashok Bhoite¹, Yuvraj K. Kanse², Supriya P. Salave³

¹Priyanka Ashok Bhoite, Department of E&TC Engineering, K.B.P.C.O.E., Satara (Maharashtra), India.

²Dr. Yuvraj K. Kanse, Associate Professor, Department of Electronics Engineering, K.B.P.C.O.E., Satara (Maharashtra), India.

³Dr. Supriya P. Salave, Assistant Professor, Department of Electronics Engineering, K.B.P.C.O.E., Satara (Maharashtra), India.

Abstract: Globally, energy sectors face the problem of electricity theft, which causes substantial financial losses, inefficiencies, and unpredictability in the energy supply. It involves the unauthorized use of electrical power through various means such as tampering with meters, bypassing meters, tapping directly into power lines, or manipulating billing mechanisms. Analyze here the performance of the proposed IoT-based system for detecting electricity theft. Show the outcomes of the alert system performance. False Positive Rate (FPR): The proportion of legitimate transactions incorrectly identified as theft. False Negative Rate (FNR): The proportion of theft events that were missed by the system. The IoT-based electricity theft detection system is quite efficient. The system’s high accuracy, precision, and recall demonstrate its ability to identify and prevent electricity theft effectively.

Keywords: Internet of Things, Electricity Theft, Meter Bypassing, Wi-Fi, Current Sensors
Scope of the Article: Recent Engineering & Technology

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Volume-14 Issue-7, June 2025 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

G110214070625

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