Browsing by Author "Khan MA"

Now showing 1 - 5 of 5
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    A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection
    (Elsevier Inc, 2023-09) Alam S; Zhao X; Niazi IK; Ayub MS; Khan MA
    Surface Electromyography (sEMG) is a technique for measuring muscle activity by recording electrical signals from the surface of the body. It is widely used in fields such as medical diagnosis, human–computer interaction, and sports injury rehabilitation. The detection of the onset and offset of muscle activation is a longstanding challenge in sEMG analysis. This study pioneers the implementation, configuration, and evaluation of Particle Swarm Optimization (PSO) against other optimization algorithms for sEMG signal detection, including Genetic algorithms (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Tabu Search (TS). The results show that the PSO algorithm achieves the highest median accuracy and F1-Score and is the fastest among the selected algorithms but has lower stability compared to Genetic algorithms and Ant colony optimization. The design and value of the cost function had a significant impact on the results, with optimal results obtained when the cost value was between 0.1203 and 0.1384. The use of these algorithms improved detection efficiency and reduced the need for manual parameter adjustment. To the best of our knowledge, no published studies have utilized Simulated Annealing, Ant colony optimization, and Tabu search meta-heuristic algorithms to detect sEMG signal onsets.
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    An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity
    (Elsevier Inc, 2023-12) Alam S; Ayub MS; Arora S; Khan MA
    Missing data can significantly impact dataset integrity and suitability, leading to unreliable statistical results, distortions, and poor decisions. The presence of missing values in data introduces inaccuracies in clustering and classification and compromises the reliability and validity of such analyses. This study investigates multiple imputation techniques specifically designed for handling missing values in ordinal data commonly encountered in surveys and questionnaires. Quantitative approaches are used to evaluate different imputation methods on various datasets with varying missing value percentages. By comparing the performance of imputation techniques using clustering metrics and algorithms (e.g., k-means, Partitioning Around Medoids), the study provides valuable insights for selecting appropriate imputation methods for accurate data analysis. Furthermore, the study examines the impact of imputed values on classification algorithms, including k-Nearest Neighbors (kNN), Naive Bayes (NB), and Multilayer Perceptron (MLP). Results demonstrate that the decision tree method is the most effective approach, closely aligning with the original data and achieving high accuracy. In contrast, random number imputation performs poorly, indicating limited reliability. This study advances the understanding of handling missing values and emphasizes the need to address this issue to enhance data analysis integrity and validity.
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    Growth performance, antibody response, and mammary gland development in New Zealand dairy replacement bovine heifers fed low or high amounts of unpasteurized whole milk
    (Oxford University Press on behalf of the American Society of Animal Science, 2022-10-28) Khan MA; Heiser A; Maclean PH; Leath SR; Lowe KA; Molenaar AJ
    This study evaluated the influence of feeding low and high preweaning allowances of unpasteurized whole milk (MA) on intake, selected blood metabolites, antibody response, mammary gland growth, and growth of New Zealand (NZ) dairy heifers to 7 mo of age. At 10 ± 2 d of age (study day 0), group-housed (six·pen-1) heifer calves (Holstein-Friesian × Jersey) were allocated to low (4 L whole milk·calf-1·d-1; n = 7 pens) or high (8 L whole milk·calf-1·d-1; n = 7 pens) MA for the next 63 d. Calves were gradually weaned between days 63 ± 2 and 73 ± 2. Calves in each pen had ad-libitum access to clean water, pelleted calf starter, and chopped grass hay from day 1 to 91 ± 2 d. At 92 ± 2 d, all calves were transferred to pasture, grazed in a mob, and their growth and selected blood metabolites were measured until day 209. All animals were weighed weekly during the indoor period (to day 91) and then at days 105, 112, 128, 162, 184, and 209. Skeletal growth measurements and blood samples to analyze selected metabolites were collected at the start of the experiment, weaning, and then postweaning on day 91, and day 201. Specific antibodies against Leptospira and Clostridia were quantified in weeks 7, 13, and 27. Mammary glands were scanned using ultrasonography at the start of the experiment, weaning, and day 201. Feeding high vs. low amounts of MA increased the preweaning growth in heifer calves (P = 0.02) without negatively affecting postweaning average daily gain (ADG) (P = 0.74). Compared with heifers fed with low MA, high MA fed heifers had a greater increase in antibodies against Leptospira and Clostridia by 13 wk of age (P = 0.0007 and P = 0.06, respectively). By 27 wk of age, the antibody response was the same in heifers offered low or high MA. There was no effect of MA on the total size of the mammary gland, measured by ultrasonography, at weaning and 7 mo of age. However, the greater MA was associated with more mammary parenchyma (P = 0.01) and less mammary fat pad (P = 0.03) in back glands at 7 mo of age compared with heifers fed lower MA. In conclusion, feeding a high vs. a low amount of unpasteurized whole milk increased the preweaning growth of New Zealand replacement heifers without negatively affecting their ADG during postweaning under grazing conditions. Feeding more (8 vs. 4 L·d-1) unpasteurized whole milk positively affected antibody responses early in life and mammary gland composition by 7 mo of age in dairy heifers reared for pasture-based dairy systems.
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    Methane emissions intensity in grazing dairy cows fed graded levels of concentrate pellets
    (Taylor and Francis Group, 2024-05-03) Bosher T; Della Rosa MM; Khan MA; Sneddon N; Donaghy D; Jonker A; Corner-Thomas R; Handcock R; Sneddon N
    The current New Zealand greenhouse gas inventory predictions assume that dairy cows consume pasture only, but the use of supplemental feeds, including concentrates, on New Zealand dairy farms has increased greatly in recent decades. The objective of this study was to evaluate the effect of feeding graded levels of concentrates on methane (CH4) emissions in lactating dairy cows within a pastoral system. Early lactation dairy cows (n = 72) were allocated (n = 18 per treatment) to receive 0, 2, 4 and 6 kg dry matter (DM) of treatment concentrates per day during milking. The cows grazed pasture ad libitum and CH4 emissions were measured in the paddocks using automated emissions monitoring systems called ‘GreenFeed’. Gross CH4 emissions (g/d) were similar for cows across the four dietary treatments, while CH4 emissions intensity (g/kg fat and protein corrected milk production (FPCM) and milk solids production) linearly decreased with increasing concentrate inclusion in the diet (P < 0.02). The CH4 intensity decreased linearly (r2 = 0.42) and quadratically (r2 = 0.53) with increasing FPCM production.
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    Predicting milk-derived hydrogel-forming peptides with TANGO
    (Elsevier Ltd, 2024-06) Khan MA; Hemar Y; Cheng K-W; Stadler FJ; De Leon-Rodriguez LM
    The uncovering of single peptides derived from food sources that can form hydrogels is of great relevance for several applications. However, identifying single peptide hydrogels from food is a daunting task given the complex nature of the food systems. The proof of concept of the applicability of TANGO, a statistical mechanical-based algorithm that predicts the β-aggregate propensity of peptides, as a tool to uncover peptides derived from milk that can form hydrogels is reported. Using TANGO in conjunction with a set of defined criteria we discovered that from a group of thirteen peptides derived from milk proteins, seven formed hydrogels at a concentration of 2 wt% and pH 7 at room temperature. Three more peptides formed aggregates and appeared to go through the syneresis process, and three additional peptides remained liquid under the experimental conditions. This result sets the basis of a simple methodology for unveiling peptide hydrogels from food and other natural sources.