Syed Inthiyaz

This study developed an MnO₂/biochar nanocomposite derived from pomegranate peel and investigated its performance for removing Rhodamine B from aqueous media. The prepared adsorbent was characterized using FTIR, BET, UV–Vis, PXRD, and FESEM analyses to determine its functional groups, porous characteristics, optical behaviour, crystalline phases, and surface morphology. A Taguchi L27 design, signal-to-noise analysis, and ANOVA were employed to examine the effects of contact time, adsorbent dosage, solution pH, and initial dye concentration. MEREC weighting and MABAC ranking were subsequently integrated for simultaneous optimization of removal efficiency and adsorption capacity. Taguchi analysis predicted the preferred factor-level combination as 60 min, pH 10, 0.05 g dosage, and 75 mg L⁻¹ concentration, whereas MABAC identified Run 24 (60 min, pH 7, 0.05 g, and 75 mg L⁻¹) as the best condition among the experimentally tested L27 alternatives, yielding 74.50% removal and 85.53 mg g⁻¹ adsorption capacity. The engineered-input ANN–GA models produced cross-validated R² values of 0.8488 and 0.9173 for removal efficiency and adsorption capacity, respectively. However, quadratic regression achieved higher leave-one-out R² values of 0.9265 and 0.9777, indicating better generalization than ANN–GA for the limited L27 dataset. The proposed framework demonstrates the combined application of statistical design, objective weighting, multi-criteria ranking, and machine learning for evaluating Rhodamine B adsorption by the MnO₂/biochar nanocomposite.

Electric vehicles (EVs) are gaining global prominence due to their high efficiency, low noise, and minimal carbon emissions. A critical aspect of EV performance lies in the interaction between energy storage systems (ESS) and power converters. Nonetheless, power delivery from storage units tends to be unreliable and needs strong converter units for effective and stable energy transmission. Several forms of direct current-to-direct current conversion systems used in electric vehicles are thoroughly examined in the paper, including both isolated and non-isolated designs such as those with the cuk, flyback, and push-pull architectures. The paper looks at converter categorization, control methods such as proportional-integral and artificial neural networks, as well as the method of modulation using unipolar and bipolar sinusoidal pulse-width modulation (PWM). Additionally, the role of optimization algorithms in improving converter performance is explored. Simulations were conducted using MATLAB/Simulink to evaluate each topology under varying load and input voltage conditions. The results demonstrate that the Push-Pull converter has the best efficiency for high-power applications, while the Cuk and Flyback converters are best for applications requiring continuous current and low-power, compact designs, respectively. This research offers insights for choosing optimal converter structures to improve energy efficiency and reliability of systems in electric vehicles.

Transformerless inverters are increasingly favored in grid-connected photovoltaic (PV) systems due to their higher efficiency, reduced size, and lower cost. This paper presents a novel transformerless inverter topology that integrates buck boost conversion with an advanced fuzzy logic controller (FLC) to enhance energy extraction and power quality under dynamically changing solar conditions. The proposed system employs a sine triangle pulse width modulation (PWM) scheme in conjunction with the FLC to improve waveform quality and system responsiveness. By dynamically adapting to variations in irradiance and load, the control strategy reduces the total harmonic distortion (THD) from 36.51% to 1.51%, significantly enhancing compliance with international grid standards. Additionally, a novel grounding technique is implemented to mitigate common mode leakage currents, a typical issue in transformerless systems, without the need for galvanic isolation. Comprehensive MATLAB/Simulink simulations validate the inverter’s performance, demonstrating superior dynamic behavior, harmonic suppression, and overall reliability. The proposed architecture offers a compact, cost effective, and high performance solution for next generation grid integrated solar PV systems.

In electric vehicles (EVs), efficient energy management is critical for reliable power transfer between the battery and motor. This paper presents the design and implementation of a bidirectional DC-DC converter equipped with a smart battery management system (BMS). The system supports bidirectional power flow, operating in boost mode during acceleration and buck mode during regenerative braking, thereby enhancing overall energy efficiency and vehicle performance. A PIC microcontroller governs the system, performing real-time monitoring of key battery parameters such as state of charge (SOC), state of health (SOH), voltage, and temperature. Safety features include automatic cooling fan activation when the temperature exceeds 45 °C and generator startup when battery voltage falls below 23 V. Real-time data is displayed via an LCD interface to improve user interaction and system transparency. The proposed system achieved a conversion efficiency of 90-93% during experimental testing, with stable switching, reliable automation, and effective thermal protection. The embedded energy management system optimizes charging and discharging cycles while preventing overcharging, deep discharge, and thermal stress. This intelligent, automated power converter enhances battery life, improves EV reliability, and contributes to sustainable transportation by enabling features like vehicle-to-grid (V2G) energy transfer. The proposed architecture is well-suited for integration into modern EV infrastructure. Although the system architecture supports future V2G integration, V2G functionality was not implemented or tested in the present experimental setup.

Design and implementation of an AC motor speed control and monitoring system based on a touch screen interface with built-in overheat protection, utilizing Arduino, meets the increasing demand for efficient, user-friendly motor control in many industrial applications. This system offers an easy-to-use interface to manage the speed of an AC motor, with real-time feedback and adjustments through a touch screen display. The system employs an Arduino microcontroller, which accepts inputs from the touch screen and processes these to regulate the motor's speed through a pulse width modulation (PWM) method. The system also has an overheat protection system, which it is able to monitor the temperature of the motor via a temperature sensor. When the motor reaches a predetermined temperature, the system automatically shuts off power to avoid damage. The intuitive touch screen facilitates convenient monitoring of motor parameters like temperature, giving a smooth experience to operators. The modular design of the system provides scalability across applications, ranging from household appliances to large industrial systems, with reliability, energy efficiency, and safety in motor-driven processes.

As liver tumor image datasets continue to grow, conventional prediction methods encounter difficulties due to significant imbalances between majority and minority classes, as well as the presence of noise. While 3D convolution effectively handles spatial data, it requires substantial GPU resources, whereas 2D convolution is limited in its ability to fully capture the third dimension. Challenges like missing data, noise, and class imbalance adversely affect classification performance, highlighting the critical role of data quality. This study introduces an enhanced ensemble classification model that utilizes k-joint probabilistic segmentation for detecting liver tumors, relying on medical imaging techniques such as CT for feature extraction and segmentation. By incorporating novel filtering and ranking approaches, the model achieves superior recall, precision, and AUC in comparison to current develops.

Effective automatic detection of multivariate and multi-class tumors is fundamental for accurately analyzing and handling diverse liver datasets. Current liver segmentation methods face significant challenges such as handling tumors of different modalities, shapes, and orientations, as well as issues with over-segmentation and incorrect tumor identification. Additionally, excessive randomness in segmented crossing areas can promote complication for the processing of segmentation as well as classification indicating to unreliable findings.To address these questions, we propose a new approach that incorporates advanced techniques for feature extraction, multivariate liver filtering, and ranking. Our solution utilizes capable classification methods under segmentation-based for increasing the detection for various tumor types in large datasets. Nevertheless,the model of Multi-Dimensional Liver and Tumor Segmentation and Classification developed is designed to accurately classify tumor-segmented sections, achieving extreme true positive (TP) rates in addition exceptional run-time productivity, measured in milliseconds.We validate the MCMVLTSC model through extensive testing using a range of statistical metrics across different liver imaging databases. The findings demonstrate that our model consistently delivers superior performance in classification accuracy and runtime efficiency compared to traditional methods.

The current state of science, technology, and industrial revolutions did not occur overnight. Many years of empirical study attempts by human intelligence have led to the world's current status. As a result, new technologies and innovations would constantly propel human civilization forward. Another outstanding invention of the present day is the brushless DC (BLDC) motor. This paper outlines the design of a BLDC motor control system utilizing MATLAB/Simulink software. The main aim of this project is to control the speed and to obtain time domain specifications of PID controller. The application of speed control of motor is vast and also required to maintain the work efficient without any disturbance, the power consumption, and any other fuel to run. On the basis of this the brushless DC motor as application is selected because of reduction in losses and also the power. The PID control system is built to control the speed of the motor and gives the precise output. The universal bridge is used to amplify the current in the output of the application. PID controller reduces the error and increases the stability of the system.

A grid-connected solar water pumping system (SWPS) uses solar power to pump water while simultaneously drawing power from the grid when necessary. These systems can benefit farmers in a variety of ways, including reliable power, lower electric bills, increased income, and improved economic viability. This study explores a solar photovoltaic (SPV) water pumping system designed to function with a single-phase distribution network. It utilizes an induction motor drive (IMD) and incorporates an advanced power-sharing technique for optimal performance. In addition to transferring power from SPV to IMD, a DC-DC boost converter functions as a grid interface and power factor adjustment device. Maximizing the power extracted from the SPV array is critical for optimizing its utilization. To do this, a control mechanism based on incremental conductance is implemented to track maximum power points. Simultaneously, the IMD connected to the power source inverter is regulated using a simple volt/frequency approach. The suggested system, which includes standalone, grid-interfaced, and mixed-mode situations, is developed and validated in a lab.