Volume-15 Issue-1, May 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 Sat, 30 May 2026 03:40:42 +0000 en-US hourly 1 https://www.ijrte.org/wp-content/uploads/2021/02/cropped-IJRTE-Logo-WS-32x32.png Volume-15 Issue-1, May 2026 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org 32 32 B837015020726 https://www.ijrte.org/portfolio-item/b837015020726/ Mon, 25 May 2026 10:32:24 +0000 https://www.ijrte.org/?post_type=portfolio&p=50111 DC-DC high-power electronic converter plays an important role in the domain of fast charging of electric vehicles. Compared to other basic DC-DC converters, the Cuk converter offers advantages such as high-power extraction capability and low ripple DC output voltage, making it more suitable for electric vehicle applications. This paper aims to design a robust, novel controller for the cuk converter to improve its reliability and efficiency compared with existing controllers. This paper presents a complete proof-of-concept frequency-domain-based design of the Lead and Lag-Lead controllers for a DC-DC Cuk converter. The proposed control scheme is being implemented in MATLAB Simulink to verify the theoretical concepts. Lastly, a comparison has been made between the open-loop and closed-loop responses of the Cuk converter to validate the proposed controller.

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Design and Development of Frequency Domain-Based Lead and Lag-Lead Controller-Based DC-DC CUK Converter for Electric Vehicle Applications
Bandan Kumar Panigrahi1, Ranjan Kumar Jena2, Durgesh Prasad Bagarty3

1Er. Bandan Kumar Panigrahi, Department of Electrical Engineering, OUTR, Bhubaneswar (Odisha), India.

2Prof. Dr. Ranjan Kumar Jena, Department of Electrical Engineering, OUTR, Bhubaneswar (Odisha), India.

3Prof. Dr. Durgesh Prasad Bagarty, Department of Electrical Engineering, OUTR, Bhubaneswar (Odisha), India.

Manuscript received on 01 May 2026 | Revised Manuscript received on 07 May 2026 | Revised Manuscript received on 10 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 26-31 | Volume-15 Issue-1, May 2026 | Retrieval Number: 100.1/ijrte.B837015020726 | DOI: 10.35940/ijrte.B8370.15010526

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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: DC-DC high-power electronic converter plays an important role in the domain of fast charging of electric vehicles. Compared to other basic DC-DC converters, the Cuk converter offers advantages such as high-power extraction capability and low ripple DC output voltage, making it more suitable for electric vehicle applications. This paper aims to design a robust, novel controller for the cuk converter to improve its reliability and efficiency compared with existing controllers. This paper presents a complete proof-of-concept frequency-domain-based design of the Lead and Lag-Lead controllers for a DC-DC Cuk converter. The proposed control scheme is being implemented in MATLAB Simulink to verify the theoretical concepts. Lastly, a comparison has been made between the open-loop and closed-loop responses of the Cuk converter to validate the proposed controller.

Keywords: Lead Controller, Lag-Lead Controller, MATLAB
Scope of the Article: Electrical Engineering

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]]> B836615020726 https://www.ijrte.org/portfolio-item/b836615020726/ Mon, 25 May 2026 10:20:05 +0000 https://www.ijrte.org/?post_type=portfolio&p=50106 Industrial environments — including factories, construction sites, warehouses, and chemical plants — continue to experience hazardous incidents due to PPE noncompliance, unauthorised zone entry, and unsafe proximity to workers. Simultaneously, web, IoT, and edge applications deployed in these environments remain vulnerable to well-documented cyber threats, including SQL Injection, XSS, and broken access control. This paper presents the Intelligent Integrated Cyber-Physical Safety and Security Framework (IICPSSF) [11], a novel hybrid edge-cloud AI system that uniquely and simultaneously enforces (i) real-time vision-based physical industrial safety monitoring, and (ii) adaptive cybersecurity design pattern enforcement — governed by a shared Unified LLM Assisted Natural Language Rule Engine (ULNLRE). The edge layer employs YOLO-E, an open-vocabulary object detection model, for promptable real-time perception at approximately 60 FPS. At the same time, a deterministic symbolic rule engine enforces auditable safety and security policies. A Pattern Knowledge Base and context aware adaptive selection engine handle cybersecurity pattern recommendations for web, IoT, and edge applications. The system achieves 92.5% precision, 90.7% F1-score, and 99.5% specificity on physical safety violation detection across four violation types, and demonstrates effective coverage of six OWASP Top 10 vulnerability classes.

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Advanced AI-Based Real-Time Industrial Safety Sentinel for Smart Hazard Detection and Workplace Safety
Snehaprabha Jadhav1, Yogini C. Kulkarni2, Pramod Jadhav3, Vinod Patil4, Amol Kadam5

1Snehaprabha Jadhav, Assistant Professor, Department of Information Technology, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.

2Prof. (Dr.) Yogini Kulkarni, Assistant Professor, Department of Information Technology, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.

3Dr. Pramod A. Jadhav, Associate Professor, Department of CSBS, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.

4Dr. Vinod H. Patil, Department of E&TC Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.

5Dr. Amol K. Kadam, Associate Professor, Department of CSBS, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.

Manuscript received on 01 April 2026 | First Revised Manuscript received on 20 April 2026 | Second Revised Manuscript received on 03 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 18-25 | Volume-15 Issue-1, May 2026 | Retrieval Number: 100.1/ijrte.B836615020726 | DOI: 10.35940/ijrte.B8366.15010526

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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: Industrial environments — including factories, construction sites, warehouses, and chemical plants — continue to experience hazardous incidents due to PPE noncompliance, unauthorised zone entry, and unsafe proximity to workers. Simultaneously, web, IoT, and edge applications deployed in these environments remain vulnerable to well-documented cyber threats, including SQL Injection, XSS, and broken access control. This paper presents the Intelligent Integrated Cyber-Physical Safety and Security Framework (IICPSSF) [11], a novel hybrid edge-cloud AI system that uniquely and simultaneously enforces (i) real-time vision-based physical industrial safety monitoring, and (ii) adaptive cybersecurity design pattern enforcement — governed by a shared Unified LLM Assisted Natural Language Rule Engine (ULNLRE). The edge layer employs YOLO-E, an open-vocabulary object detection model, for promptable real-time perception at approximately 60 FPS. At the same time, a deterministic symbolic rule engine enforces auditable safety and security policies. A Pattern Knowledge Base and context aware adaptive selection engine handle cybersecurity pattern recommendations for web, IoT, and edge applications. The system achieves 92.5% precision, 90.7% F1-score, and 99.5% specificity on physical safety violation detection across four violation types, and demonstrates effective coverage of six OWASP Top 10 vulnerability classes.

Keywords: Cyber-Physical Security, Industrial Safety Monitoring, PPE Detection, YOLO-E, Edge-Cloud Computing, Rule-Based Reasoning, Explainable AI, LLM Policy Translation, OWASP, Secure Design Patterns.
Scope of the Article: Computer Science and Engineering

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]]> A836315010526 https://www.ijrte.org/portfolio-item/a836315010526/ Mon, 25 May 2026 10:05:26 +0000 https://www.ijrte.org/?post_type=portfolio&p=50101 Agriculture remains a cornerstone of Namibia’s economy, yet small-scale crop farmers continue to face significant productivity losses due to late or inaccurate diagnosis of plant diseases. Tomato, a major crop in the country’s semi-arid regions, is highly susceptible to fungal and bacterial infections that spread rapidly under local climatic conditions. Manual inspection is labour-intensive, subjective, and ineffective for large-scale monitoring. In the literature, many studies have used high-quality datasets to train deep learning models. However, these datasets are not real-time and rarely reflect Namibia’s specific atmospheric and climatic conditions. To address this challenge, this study uses a blended dataset combining the Plant Village Tomato Leaf Dataset from Kaggle with real-time images collected from small farms in Namibia. The study further investigates a resource efficient and reliable deep learning model, namely MobileNetV2, for multiclass classification of plant diseases. The proposed framework using the MobileNetV2 model is benchmarked against the VGG16 and ResNet50 models, both trained and fine-tuned on the blended dataset. The models are compared in terms of the overall prediction accuracy from the multiclass confusion matrix and their computational cost. The results indicate that the proposed multiclass classification model based on the MobileNetV2 architecture has achieved the best performance near to 90% accuracy, compared to VGG16 (88.33%) and ResNet50 (58.02%), while incurring minimal computational cost. The model achieved fast predictions with reasonable accuracy, enabling mobile deployment to monitor crop health in the field. The results show that MobileNetV2 offers a low-cost way to assess tomato crop health and support farmers in Namibia using digital technologies.

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Resource-Efficient MobileNetV2 Model for Multiclass Plant Disease Prediction Using Real-Time Data in Smart Farming
Iita Anatolia1, Srinu Sesham2, Mateus Abisai3, Kenneth Gideon4

1Iita Anatolia, Student, Department of Electrical and Computer Engineering, University of Namibia, Ongwediva, Namibia.

2Srinu Sesham, Senior Lecturer, Department of Electrical and Computer Engineering, University of Namibia, Ongwediva, Namibia.

3Mateus Abisai, Lecturer, Department of Electrical and Computer Engineering, University of Namibia, Ongwediva, Namibia.

4Kenneth Gideon, Lecturer, Department of Electrical and Computer Engineering, University of Namibia, Ongwediva, Namibia.

Manuscript received on 28 April 2026 | Revised Manuscript received on 04 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 8-17 | Volume-15 Issue-1, May 2026 | Retrieval Number: 100.1/ijrte.A836315010526 | DOI: 10.35940/ijrte.A8363.15010526

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: Agriculture remains a cornerstone of Namibia’s economy, yet small-scale crop farmers continue to face significant productivity losses due to late or inaccurate diagnosis of plant diseases. Tomato, a major crop in the country’s semi-arid regions, is highly susceptible to fungal and bacterial infections that spread rapidly under local climatic conditions. Manual inspection is labour-intensive, subjective, and ineffective for large-scale monitoring. In the literature, many studies have used high-quality datasets to train deep learning models. However, these datasets are not real-time and rarely reflect Namibia’s specific atmospheric and climatic conditions. To address this challenge, this study uses a blended dataset combining the Plant Village Tomato Leaf Dataset from Kaggle with real-time images collected from small farms in Namibia. The study further investigates a resource efficient and reliable deep learning model, namely MobileNetV2, for multiclass classification of plant diseases. The proposed framework using the MobileNetV2 model is benchmarked against the VGG16 and ResNet50 models, both trained and fine-tuned on the blended dataset. The models are compared in terms of the overall prediction accuracy from the multiclass confusion matrix and their computational cost. The results indicate that the proposed multiclass classification model based on the MobileNetV2 architecture has achieved the best performance near to 90% accuracy, compared to VGG16 (88.33%) and ResNet50 (58.02%), while incurring minimal computational cost. The model achieved fast predictions with reasonable accuracy, enabling mobile deployment to monitor crop health in the field. The results show that MobileNetV2 offers a low-cost way to assess tomato crop health and support farmers in Namibia using digital technologies.

Keywords: Lightweight CNNs, MobileNetV2 Model, Multiclass Classification, Accuracy, Digital Technologies.
Scope of the Article: Computer Science and Engineering

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]]> A836215010526 https://www.ijrte.org/portfolio-item/a836215010526/ Mon, 25 May 2026 09:50:18 +0000 https://www.ijrte.org/?post_type=portfolio&p=50096 Medication non-adherence among elderly and visually impaired individuals often leads to missed doses, incorrect intake, and adverse health outcomes. Conventional solutions, such as pill organisers and alarm reminders, are limited in their ability to ensure accurate and timely medication administration. This paper introduces MediTrack, an automated medicine-delivery robot designed to improve adherence, enhance patient safety, and reduce caregiver dependence. MediTrack uses an Arduino microcontroller to coordinate navigation, scheduling, and dispensing. Infrared sensors enable line-following navigation along predefined paths, while ultrasonic sensors provide real-time obstacle detection and avoidance. A Real-Time Clock module schedules medication delivery at set intervals. Upon arrival at the designated location, a servo motor dispenses the correct dosage, and a buzzer provides an audible alert. An LCD module displays real-time system status for improved usability and monitoring. Experimental validation in a controlled indoor environment assessed navigation accuracy, obstacle avoidance, timing precision, and dispensing reliability. Results show consistent, timely medication delivery with minimal error and effective handling of obstacles. The automated dispensing mechanism reduces the risk of incorrect dosages and missed medications. MediTrack addresses the limitations of manual and semi-automated solutions by offering a cost-effective, reliable, and user-friendly assistive technology. This system demonstrates the potential of embedded and robotic solutions to improve medication management and quality of life for individuals with disabilities.

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MediTrack: An Automated Medication Delivery Robot for Elderly and Visually Impaired Individuals
Thushara Hameed1, Mohammed Zahir Sulaiman Al-Zakwani2, Rashid Said Hamdan Al-Jabri3, Muath Saud Saif Al-Qassabi4, Moosa Said Sulaiyam Al-Kendi5

1Thushara Hameed, Lecturer, College of Engineering and Technology, University of Engineering and Technology, UTAS, Nizwa, Sultanate of Oman.

2Mohammed Zahir Al-Zakwani, Student, College of Engineering and Technology, University of Engineering and Technology, UTAS, Nizwa, Sultanate of Oman.

3Rashid Said Aljabri, Student, College of Engineering and Technology, University of Engineering and Technology, UTAS Nizwa, Sultanate of Oman.

4Muath Saud Al-Qassabi, Student, College of Engineering and Technology, University of Engineering and Technology, UTAS, Nizwa, Sultanate of Oman.

5Moosa Said Al Kendi, Student, College of Engineering and Technology, University of Engineering and Technology, UTAS, Nizwa, Sultanate of Oman.

Manuscript received on 28 April 2026 | First Revised Manuscript received on 03 May 2026 | Second Revised Manuscript received on 10 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 1-7 | Volume-15 Issue-1, May 2026 | Retrieval Number: 100.1/ijrte.A836215010526 | DOI: 10.35940/ijrte.A8362.15010526

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: Medication non-adherence among elderly and visually impaired individuals often leads to missed doses, incorrect intake, and adverse health outcomes. Conventional solutions, such as pill organisers and alarm reminders, are limited in their ability to ensure accurate and timely medication administration. This paper introduces MediTrack, an automated medicine-delivery robot designed to improve adherence, enhance patient safety, and reduce caregiver dependence. MediTrack uses an Arduino microcontroller to coordinate navigation, scheduling, and dispensing. Infrared sensors enable line-following navigation along predefined paths, while ultrasonic sensors provide real-time obstacle detection and avoidance. A Real-Time Clock module schedules medication delivery at set intervals. Upon arrival at the designated location, a servo motor dispenses the correct dosage, and a buzzer provides an audible alert. An LCD module displays real-time system status for improved usability and monitoring. Experimental validation in a controlled indoor environment assessed navigation accuracy, obstacle avoidance, timing precision, and dispensing reliability. Results show consistent, timely medication delivery with minimal error and effective handling of obstacles. The automated dispensing mechanism reduces the risk of incorrect dosages and missed medications. MediTrack addresses the limitations of manual and semi-automated solutions by offering a cost-effective, reliable, and user-friendly assistive technology. This system demonstrates the potential of embedded and robotic solutions to improve medication management and quality of life for individuals with disabilities.

Keywords: Arduino UNO, Ultrasonic Sensor, Motor Driver, Servo Motor, Power Supply, LCD Module, RTC Module, Buzzer, NodeMCU.
Scope of the Article: Electrical Engineering

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