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The embodiment of consciousness in socio-cognitive agents play a significant role in their acceptance as co-partners. Man-machine social interaction’s success is based on the agent’s generated believable behaviors. In this regard, such an agent needs to generate realistic beliefs, intentions, goals, self-regulation, verbal, and non-verbal communication (including gestures) according to the context of ongoing interaction is very important. This study hypothesizes that the implementation of the Theory of mind (TOM) may allow the agent to change its intentions, beliefs, and desires by predicting the existing perspective and mental states of the other agents involved in the given social interaction. To study the complexity of dynamics in a social context we have taken the case study of the ‘paper-scissor-rock’ game and developed a cognitive agent capable of using gestures for non-verbal communication following the Theory of mind. This work is in progress and is being developed on the iCub robot simulation. We have used tapped delay line neural networks as the basis for reinforcement learning and strategic planning. This paper will report the cognitive model, neural network initial results obtained.

In a smart city, the subject of the congestion-free traffic has been leading objectives from the past decade, and many approaches are adopted to make congestion-free roads. These approaches and signals at one junction are not inter-linked with the signal at the previous one. Therefore, the traffic flow on the same road and at associative roads is not smooth. The study proposed a model with a hybrid computational approach in which the current signal incorporates the associative signals information. Simulation results have shown that the proposed approach gives more attractive results as compared to previously published approaches. It will help improve the flow of traffic and reduce traffic congestion.

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.

Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.

In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems. The further input variables at layer II are HBsAg, anti-HBsAg, anti-HBcAg, anti-HBcAg-IgM, HBeAg, anti-HBeAg, and HBV-DNA that determine the output condition of hepatitis such as no hepatitis, acute hepatitis, or chronic hepatitis and other reasons that arise due to enzyme vaccination or due to previous hepatitis infection. This paper presents an analysis of the results accurately using the proposed ADHB-ML-MFIS expert system to model the complex hepatitis B processes with the medical expert opinion that is collected from the Pathology Department of Shalamar Hospital, Lahore, Pakistan. The overall accuracy of the proposed ADHB-ML-MFIS expert system is 92.2%.

Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud services over the internet. The cloud service providers cannot promise accurate reliability of their services i.e. problem in provisioning of software or hardware failure etc. Reliability of cloud computing services depends on the ability of fault tolerance during the execution of services. There are so many factors can cause faults, such as network failure, browser crash, request time out or hacker attacks. When users are facing these types of faults, they usually resubmit their requests. However, if there is any key element involved in faults or errors, additional action may be needed to deal with system logs. If there is anomaly behavior occurred in faulted virtual machine, these VMs may need extra attention from cloud system protection and security point of view. In this paper, provision of reliability management in hyper-convergence cloud infrastructure is proposed and self-healing techniques in software as a service on the basis of failure in cloud services. Intelligent cloud service reliability framework will increase the reliability during execution of cloud service.