Self-Organising and Self-Learning Model for Soybean Yield Prediction

Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods

Automated Person Identification Framework Based on Fingernails and Dorsal Knuckle Patterns

Hand images are of paramount importance within critical domains like security and criminal investigation. They can sometimes be the only available evidence of an offender’s identity at a crime scene. Approaches to person identification that consider the human hand as a complex object composed of many components are rare. The approach proposed in this paper fills this gap, making use of knuckle creases and fingernail information. It introduces a framework for automatic person identification that includes localisation of the regions of interest within hand images, recognition of the detected components, segmentation of the region of interest using bounding boxes, and similarity matching between a query image and a library of available images. The following hand components are considered: i) the metacarpohalangeal, commonly known as base knuckle; ii) the proximal interphalangeal joint commonly known as major knuckle; iii) distal interphalangeal joint, commonly known as minor knuckle; iv) the interphalangeal joint, commonly known as thumb’s knuckle, and v) the fingernails. A key element of the proposed framework is the similarity matching and an important role for it is played by the feature extraction. In this paper, we exploit end-to-end deep convolutional neural networks to extract discriminative high-level abstract features. We further use BrayCurtis (BC) similarity for the matching process. We validated the proposed approach on well-known benchmarks, the ’11k Hands’ dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images known as ’PolyU HD’. We found that the results indicate that the knuckle patterns and fingernails play a significant role in the person identification. The results from the 11K dataset indicate that the results for the left hand are better than the results for the right hand. In both datasets, the fingernails produced consistently higher identification results than other hand components, with a rank-1 score of 93.65% on the ring finger of the left hand for the ’11k Hands’ dataset and rank-1 score of 93.81% for the thumb from the ’PolyU HD’ dataset

Neuroprotective effect of ranolazine improves behavioral discrepancies in a rat model of scopolamine-induced dementia

BackgroundRanolazine (Rn), an antianginal agent, acts in the central nervous system and has been used as a potential treatment agent for pain and epileptic disorders. Alzheimer’s disease (AD) is one of the most prevalent neurodegenerative diseases and the leading factor in dementia in the elderly.AimWe examined the impact of Rn on scopolamine (Sco)-induced dementia in rats.MethodsThirty-two albino male rats were divided into four groups: control, Rn, Sco, and Rn + Sco.ResultsA significant decrease in the escape latency in the Morris water maze test after pre-treatment with Rn explained better learning and memory in rats. Additionally, Rn significantly upregulated the activities of the antioxidant enzymes in the treated group compared to the Sco group but substantially reduced acetylcholinesterase activity levels in the hippocampus. Moreover, Rn dramatically reduced interleukin-1 β (IL-1β) and IL-6 and upregulated the gene expression of brain-derived neurotrophic factor (BDNF). Furthermore, in the Sco group, the hippocampal tissue’s immunohistochemical reaction of Tau and glial factor activating protein (GFAP) was significantly increased in addition to the upregulation of the Caspase-3 gene expression, which was markedly improved by pre-treatment with Rn. The majority of pyramidal neurons had large vesicular nuclei with prominent nucleoli and appeared to be more or less normal, reflecting the all-beneficial effects of Rn when the hippocampal tissue was examined under a microscope.ConclusionOur findings indicated that Rn, through its antioxidative, anti-inflammatory, and anti-apoptotic effects, as well as the control of the expression of GFAP, BDNF, and Tau proteins, has a novel neuroprotective impact against scopolamine-induced dementia in rats

The effect of high-dose vitamin D supplementation and an exercise program to lose weight on some biochemical variables of overweight women

Background and Study Aim. In recent years, there has been a technological revolution and development in all fields, particularly the sports field. This has imposed on man a lifestyle characterized by comfort at the expense of a significant portion of his movement and activities. The problem of obesity has expanded beyond the inconsistency of the body to include its direct effect of increasing the proportion of fat and the negative impact on the work of vital organs. This disruption alters the body's internal environment and causes numerous diseases. The purpose of this study is to determine the effect of a sports program on taking vitamin D to lose weight and on some biochemical variables in young adults aged 30 to 35 years old. Materials and Methods. The study involved 10 overweight women aged 30-35 years, selected through intentional sampling. Inclusion criteria required consent, good health, and no ongoing vitamin D or exercise programs. The research employed a Randomized Controlled Trial (RCT) design. Primary outcome measures encompassed body weight, body composition, lipid profile, and vitamin D status. Anthropometric measurements included age, height, weight, and training age. Biochemical measurements involved blood tests for cholesterol, triglycerides, and vitamin D levels. The experimental group received vitamin D tablets and a proposed aerobic exercise program for 12 weeks. Results. The proposed aerobic sports program with vitamin D intake improved biochemical variables, such as total fat, total cholesterol, triglyceride, high-density cholesterol, low-density cholesterol, OH, and Vitamin D-025 for the sample under investigation. The application of the aerobic program with vitamin D led to weight loss among the study's female participants. The aerobic program with vitamin D intake has a positive effect on the general health status of the sample. Conclusions. Based on the study's findings, it is recommended to consider the positive effects of Vitamin D on the overall functional state of the body, making it essential to incorporate an aerobic sports program across various age groups. Regular medical tests are crucial for monitoring and maintaining overall health. Emphasizing the importance of regular exercise is vital in preventing weight gain and reducing the risk of certain chronic diseases. By implementing these recommendations, individuals can enhance their overall well-being and lead a healthier lifestyle. To further validate these outcomes, similar studies should be conducted on different age groups and diverse samples