Volume-14 Issue-5, January 2026 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org Exploring Innovation | ISSN:2277-3878(Online) | A Periodical Journal | Reg. No.: C/819981 | Published By BEIESP Fri, 30 Jan 2026 04:19:22 +0000 en-US hourly 1 https://www.ijrte.org/wp-content/uploads/2021/02/cropped-IJRTE-Logo-WS-32x32.png Volume-14 Issue-5, January 2026 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org 32 32 E832514050126 https://www.ijrte.org/portfolio-item/e832514050126/ Sat, 24 Jan 2026 09:31:17 +0000 https://www.ijrte.org/?post_type=portfolio&p=50036 The development of smart sustainable cities increasingly relies on advanced communication infrastructures to support intelligent transportation systems, energy management, and data-intensive urban services. The rapid expansion of Internet of Everything networks has led to rising energy consumption, posing a critical challenge for sustainable urban development. This research was conducted to address the need for energy efficient, high-performance wireless communication solutions capable of supporting large-scale, innovative city applications. In particular, High Altitude Platform Systems have emerged as promising communication enablers due to their wide coverage and deployment flexibility; however, their effectiveness is constrained by the energy efficiency and performance of antenna systems. This study investigates the application of Machine Learning techniques for the optimisation of 3D antenna structures to enhance communication efficiency. The 3D intelligent Microstrip Patch Multiple Input Multiple Output antenna operating at 28 GHz was designed and optimised using a Machine Learning-driven framework. The antenna design process was carried out using 3D digital Computer Simulation Technology software, enabling precise electromagnetic modelling and performance evaluation. Machine Learning algorithms were employed to systematically adjust antenna parameters, allowing the identification of optimal design configurations beyond conventional trial-and-error methods. The performance of the optimised antenna was evaluated using Quality of Service parameters for power efficiency and last mile connectivity. Comparative analysis with non-optimised antenna designs demonstrated substantial performance gains. The results reveal an improvement of up to 31% in power efficiency, accompanied by enhanced connectivity performance. These findings indicate that Artificial Intelligence-driven antenna design is a practical approach to developing sustainable, energy-efficient communication infrastructure for future, innovative city environments.

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Machine-Learning-Driven 3D Antenna Design for Energy-Efficient HAPS in Smart Cities
Kholod D Alsufiani1, Eman S. Alkhalifah2

1Kholod D Alsufiani, Department of Computer Sciences Program, Turabah University College, Taif University, Taif, Saudi Arabia.

2Eman S. Alkhalifah, Department of Graphic Design and Digital Media, College of Arts and Design, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Manuscript received on 08 December 2025 | First Revised Manuscript received on 18 December 2025 | Second Revised Manuscript received on 01 January 2025 | Manuscript Accepted on 15 January 2026 | Manuscript published on 30 January 2026 | PP: 19-29 | Volume-14 Issue-5, January 2026 | Retrieval Number: 100.1/ijrte.E832514050126 | DOI: 10.35940/ijrte.E8325.14050126

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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 development of smart sustainable cities increasingly relies on advanced communication infrastructures to support intelligent transportation systems, energy management, and data-intensive urban services. The rapid expansion of Internet of Everything networks has led to rising energy consumption, posing a critical challenge for sustainable urban development. This research was conducted to address the need for energy efficient, high-performance wireless communication solutions capable of supporting large-scale, innovative city applications. In particular, High Altitude Platform Systems have emerged as promising communication enablers due to their wide coverage and deployment flexibility; however, their effectiveness is constrained by the energy efficiency and performance of antenna systems. This study investigates the application of Machine Learning techniques for the optimisation of 3D antenna structures to enhance communication efficiency. The 3D intelligent Microstrip Patch Multiple Input Multiple Output antenna operating at 28 GHz was designed and optimised using a Machine Learning-driven framework. The antenna design process was carried out using 3D digital Computer Simulation Technology software, enabling precise electromagnetic modelling and performance evaluation. Machine Learning algorithms were employed to systematically adjust antenna parameters, allowing the identification of optimal design configurations beyond conventional trial-and-error methods. The performance of the optimised antenna was evaluated using Quality of Service parameters for power efficiency and last mile connectivity. Comparative analysis with non-optimised antenna designs demonstrated substantial performance gains. The results reveal an improvement of up to 31% in power efficiency, accompanied by enhanced connectivity performance. These findings indicate that Artificial Intelligence-driven antenna design is a practical approach to developing sustainable, energy-efficient communication infrastructure for future, innovative city environments.

Keywords: 3D Digital Design, AI, ML, Optimisation, Smart Cities.
Scope of the Article: Computer Engineering

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]]> D831514041125 https://www.ijrte.org/portfolio-item/d831514041125/ Sat, 24 Jan 2026 09:20:08 +0000 https://www.ijrte.org/?post_type=portfolio&p=50031 Personality traits prediction from text has broad applications in various fields such as recruitment, job performance analysis, adaptive learning and personalised systems. Although traditional psychological assessments are widely used today, they may be subjective and impractical for large-scale deployment because they require the physical presence of a psychologist. This study presents an automated personality prediction model utilising text data. To address class imbalance, a significant factor that degrades model performance on the personality text dataset, a two-tier oversampling strategy has been implemented. The primary contribution of this study is to systematically evaluate the efficacy of various Deep Learning Architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTMs, for MBTI prediction. Additionally, we have explored various ensemble learning approaches by combining separable CNNs, LeNet-5, and LSTM and BiLSTM models, thereby further improving prediction accuracy and generalisation. The experimental results show that integrating the proposed oversampling technique ensemble with the ensemble learning framework achieves higher accuracy, exceeding 87%, and outperforms previous models based solely on a single architecture or machine learning methods. The proposed method enables large-scale personality assessments to be deployed anywhere, at any time, reducing the need for the physical presence of psychologists.

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Enhancing Accuracy of MBTI Personality Prediction Using Deep Ensemble Models and Data Augmentation Techniques
Devraj Patel1, Sunita V Dhavale2, Bhushan B Mhetre3

1Devraj Patel, Department of Computer Science & Engineering, Defence Institute of Advanced Technology (DU), Girinagar, Pune (MH.), India.

2Dr. Sunita Vikrant Dhavale, Associate Professor, Department of Computer Science & Engineering, Defence Institute of Advanced Technology (DU), Girinagar, Pune (M.H.), India.

3Dr. Bhushan B. Mhetre, Department of Psychiatry, Smt. Kashibai Navale Medical College and General Hospital, Narhe, Pune (MH.), India.

Manuscript received on 30 October 2025 | First Revised Manuscript received on 14 November 2025 | Second Revised Manuscript received on 17 December 2025 | Manuscript Accepted on 15 January 2026 | Manuscript published on 30 January 2026. | PP: 8-18 | Volume-14 Issue-5, January 2026 | Retrieval Number: 100.1/ijrte.D831514041125 | DOI: 10.35940/ijrte.D8315.14050126

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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: Personality traits prediction from text has broad applications in various fields such as recruitment, job performance analysis, adaptive learning and personalised systems. Although traditional psychological assessments are widely used today, they may be subjective and impractical for large-scale deployment because they require the physical presence of a psychologist. This study presents an automated personality prediction model utilising text data. To address class imbalance, a significant factor that degrades model performance on the personality text dataset, a two-tier oversampling strategy has been implemented. The primary contribution of this study is to systematically evaluate the efficacy of various Deep Learning Architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTMs, for MBTI prediction. Additionally, we have explored various ensemble learning approaches by combining separable CNNs, LeNet-5, and LSTM and BiLSTM models, thereby further improving prediction accuracy and generalisation. The experimental results show that integrating the proposed oversampling technique ensemble with the ensemble learning framework achieves higher accuracy, exceeding 87%, and outperforms previous models based solely on a single architecture or machine learning methods. The proposed method enables large-scale personality assessments to be deployed anywhere, at any time, reducing the need for the physical presence of psychologists.

Keywords: Deep Learning Ensemble, MBTI Classification, Oversampling Strategy, Personality Prediction, Text-based Assessment.
Scope of the Article: Computer Engineering

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]]> D830914041125 https://www.ijrte.org/portfolio-item/d830914041125/ Sat, 24 Jan 2026 09:07:15 +0000 https://www.ijrte.org/?post_type=portfolio&p=50026 This article presents a Machine Learning ensemble model for photovoltaic (PV) production forecasting, developed to address energy management challenges in rural tropical microgrid environments. The site studied is located in Katsepy, Madagascar, where the reliability of energy forecasts remains a significant challenge due to high climate variability. The main objective is to design a more accurate and adaptable forecasting tool to enable adequate energy planning and system operation. The methodology is based on historical photovoltaic production data and meteorological data collected between 2005 and 2023 from the PVGIS online platform. Several regression algorithms were evaluated, including Random Forests, Bagging, and Gradient Boosting, to identify the models best suited to the local context. Among these, Gradient Boosting showed the best performance according to RMSE, MAE, MAPE and R² measurements, followed closely by Random Forest and Bagging. The experimental process consists of two stages: first, validation using actual 2023 data, and then forward-looking forecasts for 2024 incorporating real temperature data. To improve accuracy and robustness, a Stacking ensemble model was constructed, combining the three best performing algorithms as base estimators and the Extra Trees Regressor as the meta-model. This ensemble approach consistently outperformed the individual models and provided realistic production estimates for 2024, indicating a moderate decline in photovoltaic production compared to 2023, driven by observed climate variations. The proposed forecasting framework provides a solid foundation for future work on optimal energy management and fault diagnosis in the Katsepy microgrid system, with great potential for adaptation to other tropical coastal regions.

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Advanced Ensemble Machine Learning for Photovoltaic Production Forecasting in Tropical Microgrids: Application on the Katsepy Site, Madagascar
Linda Christelle Mevalaza1, Andrianajaina Todizara2, Rakotoarisoa Armand Jean Claude3, Herve Mangel4, Jean Nirinarison Razafinjaka5

1Linda Christelle Mevalaza, Student, Department of Electrical and Automation Engineering, Doctoral School of Renewable Energies and Environment, University of Antsiranana, Madagascar.

2Dr. Andrianajaina Todizara, Researcher, Department of Electrical and Electronic Engineering, Higher Polytechnic School, University of Antsiranana, Maagascar.

3Dr. Rakotoarisoa Armand Jean Claude, Professor, Department of Electrical and Electronic Engineering, University of Antsiranana, Madagascar.

4Hervé Mangel, Assistant Professor, Department of Electrical and Industrial Computing Engineering, Dupuy de Lôme, Research Institute (UMR CNRS 6027 IRDL).

5Jean Nirinarison Razafinjaka, Professor, Department of Electrical and Electronic Engineering, Higher Polytechnic School, Antsiranana, Madagascar.

Manuscript received on 20 October 2025 | First Revised Manuscript received on 07 November 2025 | Second Revised Manuscript received on 16 December 2025 | Manuscript Accepted on 15 January 2026 | Manuscript published on 30 January 2026 | PP: 1-7 | Volume-14 Issue-5, January 2026 | Retrieval Number: 100.1/ijrte.D830914041125 | DOI: 10.35940/ijrte.D8309.14050126

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: This article presents a Machine Learning ensemble model for photovoltaic (PV) production forecasting, developed to address energy management challenges in rural tropical microgrid environments. The site studied is located in Katsepy, Madagascar, where the reliability of energy forecasts remains a significant challenge due to high climate variability. The main objective is to design a more accurate and adaptable forecasting tool to enable adequate energy planning and system operation. The methodology is based on historical photovoltaic production data and meteorological data collected between 2005 and 2023 from the PVGIS online platform. Several regression algorithms were evaluated, including Random Forests, Bagging, and Gradient Boosting, to identify the models best suited to the local context. Among these, Gradient Boosting showed the best performance according to RMSE, MAE, MAPE and R² measurements, followed closely by Random Forest and Bagging. The experimental process consists of two stages: first, validation using actual 2023 data, and then forward-looking forecasts for 2024 incorporating real temperature data. To improve accuracy and robustness, a Stacking ensemble model was constructed, combining the three best performing algorithms as base estimators and the Extra Trees Regressor as the meta-model. This ensemble approach consistently outperformed the individual models and provided realistic production estimates for 2024, indicating a moderate decline in photovoltaic production compared to 2023, driven by observed climate variations. The proposed forecasting framework provides a solid foundation for future work on optimal energy management and fault diagnosis in the Katsepy microgrid system, with great potential for adaptation to other tropical coastal regions.

Keywords: Forecasting, Photovoltaic Production, Tropical Microgrid, Machine Learning Model, Climate Variation.
Scope of the Article: Electrical Engineering

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