The impact of different methods of drying and preparation method ration method on the basic chemical composition of hay

The paper presents the results of three different ways of storing the dried mass of hay: bulk, small bales and large roll bales, as well as the impact of three drying methods: natural in the field, artificial drying with cold air and drying with dehumidification (warm air). In the tested meadow in the first swath, the results of chemical analyses showed differences in the method of drying hay. Regarding the tested drying method, the content of dry matter (DM) had significant differences between the storage methods as well as all variants with pre-heated air drying, where the average value of DM was in the interval of 86.18-93.01%. The content of mineral substances for certain methods of preparation and drying ranged from 5.77% to 7.72% on average. The highest content of crude proteins was in all variants of artificial drying and it ranged from 98.6 to 165.7 g/kg DM and had a statistically significant difference. Both methods of artificial post-drying had a significant impact on the cellulose content (33.76% to 28.86%) compared to drying in the traditional way because postponing the mowing time increases the cellulose content. The drying method had a statistically high significant difference on the content of neutral detergent fibres (NDF) and acidic detergent fibres (ADF), while the method of storage had no major impact. Knowledge of changes in the quality of hay during the growing season is of particular importance form the aspect of ruminant nutrition and balanced rations. The amount and quality of obtained hay is significantly affected by the time of mowing, height of mowing, swath, fertilization, floristic composition and weather conditions during drying of the green mass

Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment

Visual short-term memory (VSTM) is defined as the ability to remember a small amount of visual information, such as colors and shapes, during a short period of time. VSTM is a part of short-term memory, which can hold information up to 30 seconds. In this paper, we present the results of research where we classified the data gathered by using an electroencephalogram (EEG) during a VSTM experiment. The experiment was performed with 12 participants that were required to remember as many details as possible from the two images, displayed for 1 minute. The first assessment was done in an isolated environment, while the second assessment was done in front of the other participants, in order to increase the stress of the examinee. The classification of the EEG data was done by using four algorithms: Naive Bayes, support vector, KNN, and random forest. The results obtained show that AI-based classification could be successfully used in the proposed way, since we were able to correctly classify the order of the images presented 90.12% of the time and type of the displayed image 90.51% of the time

Audio analysis speeding enforcement techniques based on metaheuristic-optimized machine learning models

<ul><li>Datasets used in experiments for classification and regression</li><li>basic SCA for regression and classification implementation in Python</li><li>Solution class that represents swarm individual</li></ul><ul><li>Datasets used in experiments for classification and regression</li><li>basic SCA for regression and classification implementation in Python</li><li>Solution class that represents swarm individual</li></ul&gt

Renovascular Hypertension: Clinical Features, Differential Diagnoses and Basic Principles of Treatment

Renovascular hypertension is caused by renal artery stenosis. Its prevalence in populations of hypertensive patients is 1-8%, and in populations of patients with resistant hypertension, it is up to 20%. The two main causes of stenosis are atherosclerosis and fibromuscular dysplasia of the renal artery. The main clinical consequences of renal artery stenosis include renovascular hypertension, ischemic nephropathy and “flash” acute pulmonary oedema. Unilateral stenosis of the renal artery causes angiotensin II-dependent hypertension, and bilateral stenosis of the renal arteries produces volume-dependent hypertension. Renovascular aetiology of hypertension should be questioned in patients with resistant hypertension, hypertension with a murmur identified upon auscultation of the renal arteries, and a noticeable side-to-side difference in kidney size. Non-invasive diagnostic tests include the determination of concentrations of peripheral vein plasma renin activity, the captopril test, captopril scintigraphy, colour Doppler ultrasonography, computed tomography angiography, and nuclear resonance angiography. Renovasography represents the gold standard for the diagnosis of renovascular hypertension. The indications for revascularization of the renal artery include haemodynamically significant renal artery stenosis (with a systolic pressure gradient at the site of stenosis of - ΔP ≥ 20 mmHg, along with the ratio of the pressure in the distal part of the renal artery (Pd) and aortic pressure (Pa) less than 0.9 (Pd/Pa 0.8, chronic kidney disease (GFR <30 ml/min/1.73 m2) and negative captopril scintigraphy (lack of lateralization)

What are preferred water-aromatic interactions in proteins and crystal structures of small molecules?

The distribution of water molecules around aromatic rings in the protein structures and crystal structures of small molecules shows quite a small number of the strongest OH-pi interactions, a larger number of parallel interactions, and the largest number of the weakest CH-O interactions

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub