Amrita College of Engineering & Technology

Multilevel inversion describes a power conversion technique that reduces Total Harmonic Distortion (THD) by gradually increasing the output voltage and approaching a sine wave. The fundamental goal of Multi Level Inverters (MLIs) is to produce an approximate sinusoidal voltage from many levels of dc voltages, which are typically supplied from capacitor voltage sources that convert DC input voltage to AC output voltage. A key goal is to obtain a pure sinusoidal waveform at the output of the Multi Level Inverter (MLI). In a cascaded MLI, the Selective Harmonic Elimination (SHE) and Pulse Width Modulation (PWM) approach is employed to mitigate lower harmonics by maintaining the needed fundamental voltage. To determine Switching Angles (SA), an objective function is generated from the SHE problem. In this paper, the Sunflower based– Butterfly Optimization Algorithm (SF-BOA) is presented as a method for evaluating transcendental nonlinear equations using an MLI in a SHE approaches. SF-BOA’s optimized switching angle is used for 11-level three-phase PWM control using the Cascaded H Bridge architecture for harmonic reduction of the entire modulation index. Although Artificial Intelligence (AI) systems can effectively solve a non-linear transcendental equation, their time consumption together with the convergence capability differs. Enhanced Recurrent Neural Network (ERNN) shows a kind of recurrent neural network in which the hidden neurons are tweaked by SF-BOA with the goal of minimizing THD. As per the simulation data, the SF-BOA approach is much appropriate and suitable than other compared algorithms like Harris Hawks Optimization (HHO), Whale optimization algorithm, Marine Predator Algorithm (MPA), Multi Group Marine Predator Algorithm (MGMPA).

Detecting abnormal events in videos is essential for effective anomaly detection in surveillance environments. In this work, we propose a framework that combines the Abnormality-Aware Fused Attention Model (AAFAM) with the Global Density Joined Network (GDJNet) to enhance abnormal event detection. AAFAM uses spatial and channel-wise attention mechanisms to focus on anomalous regions in feature maps. It suppresses irrelevant background information. GDJNet, on the other hand, captures both local and global spatial relationships through density estimation. It allows the model to learn object distributions and co-occurrence patterns at multiple scales. To strengthen the model’s performance, we integrate AAFAM and GDJNet using a fusion strategy that combines attention and density maps, resulting in highly discriminative feature representations. Experimental results show that the proposed AAFAM–GDJNet framework outperforms existing methods and achieves state-of-the-art performance.

Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to decrease the mortality rate. Though, numerous works have addressed MI identification, an increased number suffer from over fitting and high computational burden in real-time scenarios. The proposed system introduces a novel MI detection technique using a Deep Residual Network (DRN), where the solution is optimized by the proposed Social Ski-Spider (SSS) Optimization algorithm is the novel combination of both Social Ski-driver (SSD) Optimization and the Spider Monkey Optimization (SMO). This model highly prevents the overfitting and computational burden, which increases the MI detection accuracy. Here, the proposed SSS-DRN performs detection by filtering the electrocardiography (ECG) signals. Later, the signal feature, transform feature, medical feature and statistical feature are extracted by the feature extraction phase followed by data augmentation that consists of permutation, random generation and re-sampling processes and finally, detection is accomplished by the SSS-DRN. Moreover, the developed SSS-DRN is researched for its efficiency considering metrics like accuracy, sensitivity, and specificity and observed 0.916, 0.921, and 0.926. Here, when considering the accuracy metrics, the performance gain observed by the devised model is 13.96%, 12.61%, 10.37%, 7.95%, 5%, 2.21%, and 2% higher than the traditional schemes. This indicates the devised model has high detection accuracy, which could be embedded in real-time clinical settings like hospital ECG machines, wearable ECG monitors, and mobile health applications. This improves the clinical decision-making process with increased patient outcomes.

Rising global demand for sustainable energy has driven an integration of Hybrid Renewable Energy Systems (HRES), particularly Photovoltaic (PV) and Fuel cell (FC) technologies. However, intermittency of PV power presents challenges in ensuring a stable and continuous energy supply. To address this, a High Gain Interleaved Multi-Input (HGIM) DC-DC converter is focused for efficient energy conversion and grid integration. The converter facilitates simultaneous power harvesting from PV and FC sources, optimizing power distribution through an advanced Tunicate Swarm Algorithm based Proportional Integral (TSA-PI) controller. The controller is designed to regulate voltage, ensure system stability, and enhance overall efficiency under fluctuating environmental conditions. The system is validated through multiple case studies, assessing its performance under varying solar irradiance, steady-state conditions, high grid loads, and PV shutdown scenarios. Simulation results confirm that the proposed HGIM demonstrates a peak efficiency of 96.2

In our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. Initially the social media-based information is retrieved using Enhanced Vector Space Model (EVSM). EVSMs are employed in order to characterize the text content by mapping them into high-dimensional vector spaces, capturing the relationships between words and their contextual meanings. Rigorous feature selection methods are employed to designate texts for review, and a multiclass semantic classification algorithm, specifically the HSVM classifier, is utilized for categorization. Decision tree algorithm is used along with SVM to refine the selection process. To enhance sentiment analysis accuracy, sentiment dictionaries are not only presented but also extended through the expansion of Stanford's GloVE tool. To enhance precision, the proposed work introduces weight-enhancing methods for processing renowned text weights. Sentiments are classified into positive, negative, and neutral categories. Notably, the achieved results demonstrate improved accuracy, attributed to the incorporation of an emotional sentiment enhancement factor for determining weights and leveraging sentiment dictionaries for word availability. The accuracy is obtained to be 92.78% with 91.33% positive sentiment rate and 97.32% negative sentiment rate.