Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

http://www.cs.newpaltz.edu/~lik/publications/Yuming-Xu-IS-2014.pdf

A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues

http://info.ifpan.edu.pl/~masli/Publications/PhysRep_Abs.pdf

Biomolecules under mechanical force

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

https://www.mdpi.com/2313-433X/6/8/73/pdf

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

The traditional analytical biosensor instruments are relatively bulky, expensive, and not easy to handle, thus their applications are largely limited in resource-limited settings. The recent development of microfluidic lab-on-a-chip (LOC) technology has provided a possible solution to miniaturize the conventional biosensing system, yet other accessory devices to detect, readout, analyze, transfer, and display results are still required. With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health. Based on the common analytical methods, this paper reviews the recent development of four types of smartphone based analytical biosensory systems at the POC, i.e., smartphone-based microscopic imaging, colorimetric, electrochemical, and electrochemiluminescence biosensor. The different bio-sensing strategies and analytical performance together with future perspectives are discussed.

Smartphone-based analytical biosensors

Colors are important for human for communicating with the daily encountered objects as well as his species, these colors should be represented formally and numerically within a mathematical formula so it can be projected on device/ computer storage and applications, this mathematical representation is known as color model that can hold the color space, by the means of color’s primary components (Red, Green, and Blue) the computer can visualizes what the human does in hue and lightness. In this paper; a review of most popular color models are given with the explanation of the components, color system, and transformation formula for each other, application areas and usages are also included in this work with the classification of color models according to its dependence and independence on the hardware device used in specific application, a summary of the advantages and disadvantages of the color models are also demonstrated in this work.

/pdf/understanding-color-models-a-review-2a0lcfpruo.pdf

Understanding Color Models: A Review

— The use of the gesture system in our daily life as a natural human-human interaction has inspired the researchers to simulate and utilize this gift in human-machine interaction which is appealing and can take place the bore interaction ones that existed such as television, radio, and various home appliances as well as virtual reality will worth and deserve its name, this kind of interaction ensures promising and satisfying outcomes if applied in systematic approach, and supports unadorned human hand when transferring the message to these devices which is more easiest, comfort and desired rather than the communication that requires frills to deliver the message to such devices, the gesturing is also important between human-human interaction especially with hearing impaired, deaf and mute peoples, in this study, we have presented different researches that done in this area regarding the geometric features which considered as a live features compared with non-geometric features which considered as blind features, and we have focused on the researches gathered to achieve this important link between human and his made machines, also we have provide our algorithms for overcome some shortcomings existed in some mentioned algorithms in order to provide a robust gesture recognition algorithm that does not have a rotation hinder which most of current algorithms have.

Hand Gesture Modeling and Recognition using Geometric Features: A Review

We consider a model of self-avoiding walk on a lattice with on-site repulsion and an attraction for every vertex of the walk visited on the surface to study force-induced desorption of a linear polymer chain adsorbed on an attractive surface and use the exact enumeration technique for analyzing how the critical force for desorption fc(T) depends on the temperature. The curve fc(T) gives the boundary separating the adsorbed phase from the desorbed phase. Our results show that in two dimensions, where surface is a line, the force fc(T) increases monotonically as temperature is lowered and becomes almost constant at very low temperatures. In case of three dimensions we, however, find re-entrance, i.e. fc(T) goes through a maximum as temperature is lowered. The behaviour of the polymer chain at different values of temperature and force is examined by calculating the probability distribution of the height from the surface of the vertex at which external force is applied.

Force-induced desorption of a linear polymer chain adsorbed on an attractive surface

We use the lattice model of directed walks to investigate the conformational as well as the adsorption properties of a semiflexible homopolymer chain immersed in a good solvent in 2D and 3D. To account for the stiffness in the chain we have introduced energy barrier for each bend in the walk and have calculated the persistent length as a function of this energy. For the adsorption on an impenetrable surface perpendicular to the preferred direction of the walk we have solved the model exactly and have found the critical value of the surface attractions for the adsorption in both 2D and 3D. We have also enumerated all possible walks on square and cubic lattices for the number of steps N⩽30 for 2D and N⩽20 for 3D and have used ratio method for extrapolation. The transition located using this method is in excellent agreement with the results found from the analytical method. We have compared the results of surface adsorption for two different surface orientations. In one of the orientation, surface is considered parallel to the preferred direction and in another it is perpendicular to the preferred direction. Results found in both the cases indicate that for stiffer chains adsorption transition takes place at higher temperature compared to that of flexible chain

A simple and exactly solvable model for a semiflexible polymer chain interacting with a surface

Virus based epidemic is one of the speedy and widely spread infectious disease which can affect the economy of the country as well as it is life-threatening too. So, there is a need to forecast the epidemic lifespan, which can help us in taking preventive measures and remedial action on time. These preventive measures and corrective action may consist of closing schools, closing malls, closing theaters, sealing of borders, suspension of public services, and suspension of traveling. Resuming such restrictions is depends upon the outbreak momentum and its decay rate. The accurate forecasting of the epidemic lifespan is one of the enormously essential and challenging tasks. It is a challenging task because the lack of knowledge about the novel virus-based diseases and its consequences with complicated societal-governmental factors can influence the widespread of this newly born disease. At this stage, any forecasting can play a vital role, and it will be reliable too. As we know, the novel virus-based diseases are in a growing phase, and we also do not have real-time data samples. Thus, the biggest challenge is to find out the machine learning-based best forecasting model, which could offer better forecasting with the limited training samples. In this paper, the Multi-Task Gaussian Process (MTGP) regression model with enhanced predictions of novel coronavirus (COVID-19) outbreak is proposed. The purpose of the proposed MTGP regression model is to predict the COVID-19 outbreak worldwide. It will help the countries in planning their preventive measures to reduce the overall impact of the speedy and widely spread infectious disease. The result of the proposed model has been compared with the other prediction model to find out its suitability and correctness. In subsequent analysis, the significance of IoT based devices in COVID-19 detection and prevention has been discussed.

/pdf/enhanced-gaussian-process-regression-based-forecasting-model-1ayh2d77o0.pdf

Pramod Kumar Mishra

Papers

Understanding Color Models: A Review

Hand Gesture Modeling and Recognition using Geometric Features: A Review

Force-induced desorption of a linear polymer chain adsorbed on an attractive surface

A simple and exactly solvable model for a semiflexible polymer chain interacting with a surface

Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection