Sant Longowal Institute of Engineering and Technology (abbreviated SLIET i.e.ਸਲਾਇਟ सलाइट ) is a Govt. of India established (1989) deemed university under Section 3 of the UGC Act 1956 for higher education and research in India. The UG Program of SLIET is accredited as TIER 1 by the NBA ( National Board of Accreditation). It is well known as the "Modern Gurukul" of Tech Education due to lush green campus of 451 acres (183 ha) in Longowal, Sangrur, Punjab, India. SLIET is fully funded by the Ministry of Human Resource Development, and is an autonomous body controlled by the SLIET Society. Institute has been set up in the memory of Late Sh. Harchand Singh ji Longowal under Rajiv Longowal Punjab accord. Educational opportunities include technical and practical training in the fields of engineering and technology. The students and alumni of SLIET are informally referred to as SLIETians..
One of the key challenges in the field of data clustering is the identification of cluster centroid positions to form compact and well-separated clusters. To address this challenge, this work proposes the hybrid opposition-based improved hiking optimization algorithm (HOB-iHOA) for partitional data clustering. The proposed algorithm employs a hybrid approach for initializing cluster centroids by integrating random estimation, quasi-opposition-based learning, and K-Means to improve exploration capability, ensuring diversity and identifying various promising regions in the feature space. Moreover, this algorithm balances exploration and exploitation using momentum-driven updates combined with logistic map chaos, promoting stable convergence of cluster centroids for improved clustering solutions. The performance of HOB-iHOA is evaluated on 23 benchmark functions, comprising unimodal, multimodal, and fixed-dimension multimodal functions, against seven state-of-the-art (SoA) algorithms. Furthermore, its applicability is tested on three real-world engineering design problems, demonstrating its reliability. Finally, the effectiveness of HOB-iHOA in data clustering problem is evaluated on thirteen real-world datasets from the UCI Machine Learning Repository and the results obtained with HOB-iHOA are compared with existing metaheuristic-based SoA clustering algorithms using both internal and external validation metrics. The HOB-iHOA improves the performance of data clustering by reducing the sum of intra-cluster distances and the Davies–Bouldin index and by increasing the silhouette coefficient and adjusted rand index scores on benchmark datasets. 3D scatter plots, convergence curve analysis, ablation studies, and runtime comparisons are conducted to show HOB-iHOA suitability for practical deployment. Additionally, scalability, statistical robustness and parameter sensitivity analyses of HOB-iHOA have been performed. The results demonstrate that HOB-iHOA effectively discovers optimal cluster centroid positions, forming compact and well-separated clusters that provide deeper insights into the datasets.
Data clustering is a prominent unsupervised learning method that continues to attract significant research interest due to its diverse applications across various domains. Partitional clustering methods like K-Means optimize a criterion function to identify homogeneous groups in a dataset. However, random initialization can lead to non-optimal cluster centroids. While combining K-Means with metaheuristics improves performance in data analysis tasks, the basic learning strategies, decline in diversity and inadequate exploration and exploitation in population-based metaheuristic algorithms still cause premature convergence of cluster centroids to local optima. To address this challenge, this work proposes the hybrid opposition-based improved particle swarm optimization algorithm (HOB-iPSO) for partitional data clustering. HOB-iPSO employs a hybrid approach for initializing cluster centroids by integrating random estimation, quasi-opposition-based learning, and K-Means to improve exploration capability, maintain diversity, and identify various promising regions in the high-dimensional feature space. Moreover, HOB-iPSO achieves a balanced exploration–exploitation using logistic map-based chaotic inertia weight, promoting stable convergence of cluster centroids for improved data clustering solutions. The effectiveness of HOB-iPSO is evaluated on thirteen real-world datasets from the UCI Machine Learning Repository, and the results obtained with HOB-iPSO are compared with K-Means and existing metaheuristic-based data clustering algorithms using both internal and external validation metrics. The proposed HOB-iPSO improves the performance of data clustering by reducing the sum of intra-cluster distances and the Davies–Bouldin index and by increasing the silhouette coefficient, F-measure, and accuracy, compared to the other data clustering algorithms. Statistical significance is evaluated using the Friedman test, followed by Wilcoxon signed-rank post hoc tests with Holm correction. The numerical results and graphical visualizations, including ablation studies, runtime comparison for practical applicability, convergence curve analysis, and 3D scatter plots, confirm that the HOB-iPSO is reliable and effective in producing well-formed clusters.
Spring onion (Allium fistulosum) is a versatile vegetable with numerous phytochemicals; however, a comprehensive bioactive profiling of the crop using the most appropriate extraction solvent in combination with integrated HS-SPME-GC-MS and LC-ESI-QTOF-MS/MS analyses has not yet been reported. Therefore, this study aimed to investigate the detailed phytochemical profile of three highly cultivated and consumed spring onion varieties from North India. These varieties were tested with various solvents, including ethanol, methanol, acetone, and distilled water (1:20/24H/RT) for phytochemical and antioxidant analysis. Distilled water and 50 All parts of spring onion varieties varied significantly in bioactive composition. Distilled water and 50
The long-term stability of anthocyanins (ACs) and the interaction mechanism of microwave-irradiated modified black bean protein isolates (BBPI) with hydroxypropyl methyl cellulose (HPMC) remain unclear. This study investigates a freeze-drying (FD) microencapsulation strategy using BBPI (10–15 Encapsulation of anthocyanins (ACs) from black bean seed coat using freeze drying. BBPI/HPMC/ACs enhanced encapsulation efficiency above 80
Pea pod peels, a byproduct of pea processing, are recognized for their nutritional and therapeutic potential. However, drying methods can significantly impact their nutritional composition and bioactive properties. This research evaluated impact of sun drying and hot-air drying on pea pod peels. Analyses included nutritional content, bioactive compounds, techno-functional properties, microstructure (SEM), crystallinity (XRD), functional groups (FTIR), thermal stability (TGA) and in vitro protein digestibility. Hot-air drying retained 4.1 Pea pod peels are significant dietary fiber source, protein, carbohydrates and polyphenols. In both pea pod powders, anti-nutrients were within threshold limit. Strong practical properties were demonstrated by pea pods. Pea pod peels can be used in food recipes as possible natural ingredient.
