Polypyrrole–neutravidin layer for impedimetric biosensor

Polypyrrole synthesis and neutravidin entrapment inside the polymer were performed simultaneously in one step using a controlled anodic current passing. The incorporation of neutravidin within the electrogenerated film was confirmed by FTIR and impedance spectroscopy. Biotinylated anti-triazine Fab fragment was attached to the PPy/neutravidin modified electrode throughout the well-studied biotin–neutravidin interaction. During impedance measurements, the charge transfer resistance increased gradually with atrazine concentration whereas no significant response was recorded after rabbit IgG injection. The immunosensor was very sensitive to atrazine antigen in the range of 0.1–200 ng/ml and the detection limit attained 0.1 ng/ml.     

Personality Detection Using Context Based Emotions in Cognitive Agents

Detection of personality using emotions is a research domain in artificial intelligence. At present, some agents can keep the human’s profile for interaction and adapts themselves according to their preferences. However, the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject. The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior. In our daily life, humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input. This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input, using the context subjectivity of the given data and emotions obtained from a particular situation/context. The proposed work consists of Jumbo Chatbot, which can chat with humans. In this social interaction, the chatbot predicts human personality by understanding the emotions and context of interactive humans. Currently, the Jumbo chatbot is using the BFI technique to interact with a human. The accuracy of proposed work varies and improve through getting more experiences of interaction.     

Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults who have excessively spent time on social media which necessitates a thorough psychological analysis of all these platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, and hypomania) commonly found in adults due to extreme use of social media networks. To classify the psychotic issues with the user's mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN). The used ML models are trained and tested by using different combinations of features selection techniques. To observe the most suitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as ‘Suitability’) has been used which combines the accuracy of the model with its execution time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.     

Multiatlas‐based segmentation of female pelvic organs: Application for computer‐aided diagnosis of cervical cancer

Atlas‐based segmentation is a high level segmentation technique which has become a standard paradigm for exploiting prior knowledge in image segmentation. Recent multiatlas‐based methods have provided greatly accurate segmentations of different parts of the human body by propagating manual delineations from multiple atlases in a data set to a query subject and fusing them. The female pelvic region is known to be of high variability which makes the segmentation task difficult. We propose, here, an approach for the segmentation of magnetic resonance imaging (MRI) called multiatlas‐based segmentation using online machine learning (OML). The proposed approach allows separating regions which may be affected by cervical cancer in a female pelvic MRI. The suggested approach is based on an online learning method for the construction of the dataset of atlases. The experiments demonstrate the higher accuracy of the suggested approach compared to a segmentation technique based on a fixed dataset of atlases and single‐atlas‐based segmentation technique.     

Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control

In this paper, the optimal strategies for discrete-time linear system quadratic zero-sum games related to the H-infinity optimal control problem are solved in forward time without knowing the system dynamical matrices. The idea is to solve for an action dependent value function Q(x,u,w) of the zero-sum game instead of solving for the state dependent value function V(x) which satisfies a corresponding game algebraic Riccati equation (GARE). Since the state and actions spaces are continuous, two action networks and one critic network are used that are adaptively tuned in forward time using adaptive critic methods. The result is a Q-learning approximate dynamic programming (ADP) model-free approach that solves the zero-sum game forward in time. It is shown that the critic converges to the game value function and the action networks converge to the Nash equilibrium of the game. Proofs of convergence of the algorithm are shown. It is proven that the algorithm ends up to be a model-free iterative algorithm to solve the GARE of the linear quadratic discrete-time zero-sum game. The effectiveness of this method is shown by performing an H-infinity control autopilot design for an F-16 aircraft.     

Mechanical and thermal properties of graphite platelet/epoxy composites

Anhydride-cured diglycidyl ether of bisphenol A (DGEBA) reinforced with 2.5-5% by weight graphite platelets was fabricated. The structural, mechanical, viscoelastic and thermal properties of these composites were studied and compared. XRD studies indicated that the processing of composites did not change the original d-spacing of pure graphite. Tensile property measurements of composites indicated higher elastic modulus and tensile strength with increasing concentration of graphite platelets. The storage modulus and glass transition temperatures (T_g) of the composites also increased with increasing platelet concentration, however, the coefficient of thermal expansion decreased with the addition of graphite platelets. The thermal stability was determined using thermogravimetric analysis. The composites showed higher thermal stability in comparison with pure epoxy and increased char concentration for higher graphite concentration. The effects of reinforcement on the damage mechanisms of these composites were investigated by scanning electron microscopy.     

Robust Design of an Anticipative Feedforward-feedback Controller for MIMO Systems

International Journal of Control, Automation and Systems - In this paper, multi-input multi-output preview systems, for which future values of the reference and disturbance signals are known a...     

The simplex simulation as a tool to reveal publication strategies and citation factors

Scientometrics - Scientists’ evaluations are commonly made by h-index calculated from citation levels of published papers. Although single index way is simple, synthetic, rapid and popular,...     

Sol–Gel Auto-combustion Preparation of M2+ = Mg2+, Mn2+, Cd2+ Substituted M0.25Ni0.15Cu0.25Co0.35Fe2O4 Ferrites and Their Characterizations

Journal of Superconductivity and Novel Magnetism - Cost-effective and controllable synthesis of M0.25Ni0.15Cu0.25Co0.35Fe2O4 (M2+ ?=?Mg2+, Mn2+, and Cd2+) ferrites via the sol–gel...