Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique

Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step toward a specific goal; sensing and avoiding the obstacles coming across its way toward the target by training to take the corrective action for every possible offset is one of the most challenging problems in the field of robotics. This problem is solved by proposing an Artificial Intelligence System (AIS), which works on the behaviour of Intelligent Autonomous Vehicles (IAVs) like humans in recognition, learning, decision making, and action. First, the use of the AIS and some navigation methods based on Artificial Neural Networks (ANNs) to training datasets provided high Mean Square Error (MSE) from training on MATLAB Simulink tool. Standardization techniques were used to improve the performance of results from the training network on MATLAB Simulink. When it comes to knowledge-based systems, ANNs can be well adapted in an appropriate form. The adaption is related to the learning capacity since the network can consider and respond to new constraints and data related to the external environment.

Norms and standardizations in neuropsychology via equivalent scores: software solutions and practical guides

Background Norming neuropsychological tests and standardizing their raw scores are needed to draw objective clinical judgments on clients’ neuropsychological profile. The Equivalent Score (ES) method is a regression-based normative/standardization technique that relies on the non-parametric identification of the observations corresponding to the outer and inner tolerance limits (oTL; iTL) — to derive a cut-off, as well as to between-ES thresholds — to mark the passage across different levels of ability. However, identifying these observations is still a time-consuming, “manual” procedure. This work aimed at providing practitioners with a user-friendly code that helps compute TLs and ES thresholds. Methods R language and RStudio environment were adopted. A function for identifying the observations corresponding to both TLs by exploiting Beta distribution features was implemented. A code for identifying the observations corresponding to ES thresholds according to a z-deviate-based approach is also provided. Results An exhaustive paradigm of usage of both the aforementioned function and script has been carried out. A user-friendly, online applet is provided for the calculation of both TLs and ESs thresholds. A brief summary of the regression-based procedure preceding the identification of TLs and ESs threshold is also given (along with an R script implementing these steps). Discussion The present work provides with a software solution to the calculation of TLs and ES thresholds for norming/standardizing neuropsychological tests. These software can help reduce both the subjectivity and the error rate when applying the ES method, as well as simplify and expedite its implementation.

SDFS: A Standardization Technique for Nonparametric Analysis

Due to availability of computational tools for data acquisition, it is very easy to collect many dimensions from an object. Nevertheless, data acquisition from an object in an experiment may have a low number of dimensions. The analysis of low dimensional data has break-through role. But raw and sparse nature of dataset imposes new challenges and requirements for data analysis due to their special and unique characteristics. In the process of overall characterization of low-dimensional data, the data pre-processing plays crucial role. One of the first processes is normalization and standardization process. Therefore, in this paper, I would like to propose novel standardization technique called SDFS (Standardization for Distribution Free Statistics) for nonparametric data analysis. This technique is robust for small sample size with missing values of data points, which commonly exist in real time experiments lead to sparse low-dimensional data. The comprehensive experimental evaluation shows that SDFS standardization is significantly outperforms on existing standardization methods.

Using machine learning algorithms to detect frauds in telephone networks

This paper presents an analysis of voice traffic in telephone networks, based on machine learning algorithms to detect frauds made by callers. Starting from the raw data set that includes information about the call date, destination number, duration and caller's number, in our approach we were able to identify fraudulent calls in early stages. For balance, the data set was split in 2 parts: one for training and one for testing. To obtain mean’s values from dataset, a standardization technique was applied in order to scale the data before the dimensionality reduction using Principal Component Analysis. Then, the first two components were used as inputs for Logistic Regression and Random Forest models, having the caller as target. Finally, the target was moved on the destination file so as to identify the caller and the moment when the call has started based on a vector representation of words.

Phytochemical Markers: Classification, Applications and Isolation

Background: There has been aroused demand for herbal drugs/products worldwide because of their fewer side effects as compared to synthetic drugs. The major obstacle in the global acceptance of herbal products is the lack of proper standardization technique. Methods: Various test procedures have been used for authentication and quality control of botanicals among which marker based standardization has attained more attention. The major challenge faced by phytochemist is to select appropriate phytochemical marker for quality control of herbal drugs. Phytochemical markers used for standardization must be of known purity. Phytochemical markers which are not commercially available have to be isolated from respective medicinal plants. Various chromatographic techniques are reported for the purification of phytomarkers from plants. A comprehensive report on different purification techniques of isolation of phytochemical markers through in-depth review of scientific literature is required. Conclusion: This article highlights various classifications of phytochemical markers along with their applications in standardization of herbal drugs and various classical and modern analytical techniques for their isolation.

The Effect of Consuming a Liquid Diet vs a Solid Diet 24-hr Preexperimental Trials on Adherence in Athletes

Discrepancies in energy and macronutrient intakes between tests are apparent even when a solid prepackaged diet (Sdiet) is used to standardize dietary intake for preexperimental trials. It is unknown whether a liquid prepackaged diet (Ldiet) leads to improved adherence, resulting in lower variability in energy and macronutrient intakes. This study assesses the ability of athletes to replicate a diet when an Ldiet or Sdiet was used as a dietary standardization technique. In a cross-over design, 30 athletes were randomly assigned to either Sdiet or Ldiet. Each diet was consumed for two nonconsecutive days. Participants were instructed to consume all the meals provided and to return any leftovers. The coefficient of variation (CV) was calculated for each nutrient for the two methods and reported as the average CV. The Bland–Altman plots show that differences between Days 1 and 2 in energy and macronutrient intakes for both diets were close to zero, with the exception of some outliers. The %CV for Sdiet was higher than Ldiet (5% and 3% for energy, 5% and 3% for carbohydrate, 5% and 2% for protein, and 5% and 3% for fat, respectively). There was a strong positive correlation for energy and all macronutrients between Days 1 and 2 for both methods (r > .80; p < .05). Ldiet is an effective technique to standardize diet preexperimental trials and could be used as an alternative to Sdiet. Furthermore, Ldiet may lead to additional improvements in the compliance of participants to the diet and also decrease the cost and time of preparation.

Physiochemical Characterization of Herbo-Metallic NanoMedicine-Abhrak Bhasma by Ayurvedic and Novel Techniques

The Ideal Physicochemical nature of the Bhasma preparation in the Indian System of medicine (ISM) is not as clear required as per the regulatory conditions, hence the analytical studies are one of the significant parts of drug standardization in ISM, such as Siddha, Ayurveda and Unani. The Metallic medicine used in the treatment of disease in human need scientific community for its Authentication, potential development and regulatory aspects. The Present investigation deals with the Physio-chemical evaluation of the ayurvedic medicine Abhrak bhasma by various traditional and novel techniques. In ayurvedic procedure the nature of the bhasmahad been studied by various techniques to determine the quantitative and qualitative level for standardization purpose. In the Present study the Bhasma scrutinized for Physical nature by Varna (Colour), Varitara (Floating), Rekhapurva (furrow filling), Anjana sannibha (softness), Nischandra (Lusterless), Sukshmatva (fineness) and novel techniques such as Nature, Hardness, Solubility, Ash (Acid insoluble), LOD, Assay for determination of percentage silicon dioxide, Heavy metals and arsenic content. The chemical nature is determined by Amla pareeksha (bitter taste), Gatarasatva (absence of taste), Aksharatwa (absence of alkaline taste), Nirdhuma (absence of smoke) and by novel technique such as SEM, Raman spectroscopy, NMR and EDAX. The Abhrak bhasma fulfilled the traditional and novel standardization technique and confirm the presence of Metal oxides of Si, Ca, Fe, Al. NMR studies show the presence of characteristic peak. Therefore the quality of the Abhrak bhasma was customary by both traditional and novel standardization technique.