International Conference on Science, Engineering & Management Trends Nov 2025

Comparative Performance Evaluation Of A Proposed Controller Tuning Strategy Against Conventional, Fuzzy Logic, And Optimization-Based Methods Using Key Control Indices

Authors: Asma Shibli, Dr Mohd Ilyas, Prof. Anwar Shahzad Siddiqui

Abstract: Precise control performance in dynamic and nonlinear systems remains a significant challenge for traditional PID controllers, primarily due to their fixed-gain nature and limited adaptability under varying operating conditions. This paper presents a fuzzy gain-scheduled PID tuning strategy that dynamically modifies the proportional, integral, and derivative gains based on real-time error and change in error using fuzzy logic inference. The proposed controller integrates the simplicity of a classical PID with the intelligent adaptability of fuzzy reasoning to enhance system stability, minimize overshoot, and accelerate transient response. A comparative performance evaluation is conducted against conventional tuning methods (Ziegler–Nichols and Cohen–Coon), fuzzy logic controllers, and optimization-based approaches (Genetic Algorithm and Particle Swarm Optimization) using standard control performance indices such as rise time, settling time, peak overshoot, steady-state error, and integral error metrics (IAE, ISE, ITAE). Simulation results validate that the proposed fuzzy gain scheduling method significantly improves dynamic performance and robustness while reducing steady-state error and control effort. The results demonstrate that the proposed approach offers an effective and computationally efficient tuning mechanism suitable for real-time applications in nonlinear and time-varying systems.

DOI: http://doi.org/10.5281/zenodo.17431856

Prediction Of Plant Leaf Health By Image Contour Segmentation And LSTM Model

Authors: Arpit Pethe Scholar, Arjun Rajput Assistant Prof, Dr. Sanjay Sharma, Dr. Sanjay Sharma

Abstract: Agriculture is the backbone of the Indian economy and contributes significantly to the GDP. Crop diseases, particularly those affecting leaves, lead to a decline in both the quality and quantity of agricultural produce. Traditional methods like expert diagnosis and pathogen analysis depend on skilled professionals and may be time-consuming. These approaches are also prone to human errors, affecting the accuracy of disease identification and management. This paper has proposed Plant Leaf Heath Prediction Model (PLHPM) leaf health prediction model that segment input image and extract features for learning. Image segmentation was done by active contour method, while histogram features was extracted from the image. Extracted histogram features were used for the LSTM model training. Experiment was done on real dataset images of potato. Result shows that proposed Plant Leaf Heath Prediction Model (PLHPM) has increases the detection precision and accuracy in less execution time.

DOI: http://doi.org/10.5281/zenodo.17440544

Ai-Based Patient Health Monitoring with Real Time Alert and Rescue System

Authors: Charumathi S, Kanishka Shree N, Thanuja G A

Abstract: This paper presents the design and implementation of an AI-based Patient Health Monitoring System that integrates symptom prediction, appointment and specialist mapping, OCR- based prescription parsing, medication reminders, and an auto- mated emergency alert-and-rescue mechanism with live location sharing. The system employs NLP for symptom understanding, machine learning classifiers for disease probability estimation, OCR for prescription digitization, and an event-driven notifica- tion architecture for fast emergency response. Emphasis is placed on real-time capability, data security, and practical integration with healthcare workflows. We demonstrate the system architec- ture, algorithms, implementation details, and an evaluation plan for accuracy and responsiveness.

Seasonal Variation In Heavy Metal Concentration In Telfairia Occidentalis Leaves In Ibeno Local Government Area, Akwa Ibom State

Authors: Erienu Obruche Kennedy, Onwugbuta Godpower Chukwuemeka, Njor Oru Ogar, Clark Poro David, Alani Olubukola Anuoluwapo, Essiet Akanimo Gordon, Apuyor Kingsley Efe

Abstract: The concentrations of heavy metals in the Ibeno Local Government Area of Akwa Ibom State, Nigeria were examined. This study employed an experimental design methodology. In December 2024 and June 2025, fifteen composite samples of Telfairia occidentalis leaves were collected. The leaf samples underwent washing with de-ionized water, were dried to a constant weight in an oven at 105 °C, and then pulverized to achieve a 2 mm mesh size for subsequent analysis. The ground leaves were digested using 1.0 cm3 of concentrated HClO4, 5 cm3 of concentrated HNO3, and 0.5 cm3 of concentrated H2SO4 in a 50 cm3 Kjeldahl flask. The concentration of heavy metals was determined using Atomic Absorption Spectroscopy. The data were analyzed based on the first-order kinetic model InC = InCo – kt. The concentrations of heavy metals (mg kg-1) during the dry season were: Mn (7.73 ± 3.06), Fe (5.93 ± 1.28), V (0.16 ± 0.26), Cd (0.21 ± 0.16), Ni (0.02 ± 0.01), while during the wet season, they were: Mn (7.75 ± 3.76), Fe (5.96 ± 4.07), V (0.21 ± 0.09), Cd (0.19 ± 0.06), Ni (0.03 ± 0.06). The results indicated that the concentrations of heavy metals varied between the wet and dry seasons. The mean concentrations of certain heavy metals (Ni, V, Pb, Zn, and Co) in the leaves of Telfairia occidentalis fell within the acceptable range of WHO standards for vegetables and food products, with the exception of Cd, Fe, and Mn. In conclusion, Telfairia occidentalis can serve as a resident indigenous plant bioindicator for monitoring anthropogenic influences of V, Pb, Mn, and Zn in the soil of the study area.

DOI: http://doi.org/10.5281/zenodo.17600209

Personal Voice Assistant Robot

Authors: Shwethaa.A.R, Ashwini GV, Samarth R Biradar, Shrikant Ravi Rathod, Sowmya G, Yamanur

Abstract: This project focuses on the development of a multifunctional Personal Assistant Robot designed to perform autonomous floor cleaning and home automation tasks. The system integrates various sensors, actuators, and controllers to navigate indoor environments, detect obstacles, and execute tasks such as vacuuming, mopping, and controlling lights or appliances. The robot can be operated using voice commands or a mobile application, enhancing user convenience and supporting smart home integration. By combining real-time decision-making with automation, this robot serves as a cost-effective and efficient solution for modern households, particularly aiding elderly or physically challenged individuals.