A new and simple optimization algorithm known as Varna-based Optimization (VBO) is introduced in this paper for solving optimization problems. It is inspired by the human-society structure and human behavior. Varna (a Sanskrit word, which means Class) is decided by people’s Karma (a Sanskrit word, which means Action), not by their birth. The performance of the proposed method is examined by experimenting it on six unconstrained, and five constrained benchmark functions having different characteristics. Its results are compared with other well-known optimization methods (PSO, TLBO, and Jaya) for multi-dimensional numeric problems. Our experimental results show that the VBO outperforms other optimization algorithms and have proved the better effectiveness of the proposed algorithm.

In general, medical clustering concerns a big database. The present paper aims at extracting a fuzzy linguistic summary from a large medical database. A linguistic summary is used to reduce large volumes of data to simple sentences. It is worth noting that with the increase of the amount of medical data, different techniques of machine learning have been developed recently.
In this article, an attempt is made to build a medical linguistic summary template. Our linguistic summary model is based on the calculated fuzzy cardinality. It deals with semantic queries in natural language.
Our proposal is to develop a classification system based on the linguistic summary of two medical databases in which the calculation of similarity between different sets of linguistic summaries is used; the patient’s class is then identified by calculating the Sugeno integral.
The present study was successful in developing a classification system that is based on the linguistic summary of two datasets from the UCI Machine Learning Repository, i.e. Pima Indians
Diabetes dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The results obtained were then employed for a benchmark test.

As elliptic curve cryptography is one of the popular ways of constructing an encoding and decoding processes, public-key algorithms as its basis provide people a comfortable way of exchanging pieces of encoded information. As the time goes by, a lot of algorithms have emerged, some of them are still in use today; some others are still being developed into new forms. The main point of algorithm innovation is to reduce the number of processed operations during every possible step to find maximum efficiency and highest speed while performing the calculations. This article describes an improved method of the López-Dahab-Montgomery (LD-Montgomery) scalar point multiplication in terms of working with binary elliptic curves. It is shown in the article that the possible improvement lies in reordering the set of operations which is used in LD-Montgomery scalar point multiplication algorithm. The algorithm is used to compute point multiplication results of the curves over binary Galois Fields featuring the following m values: . The article also presents the experimental results based on different scalars.

Modern Integrated circuits (ICs) suffer from excessive power and temperature issues because of embedding a large number of applications on small silicon real estate. Low power technique is introduced to reduce the power. With the reduction of power, area of circuit increases and vice versa. It shows a trade-off nature between them. Increase of area is against the trend of technology scaling which demands small area. Due to small area and high power dissipation, power-density increases. As power-density is directly converging into temperature, it emerges as a challenge in front of the VLSI design engineer to minimize the effect of temperature by reducing power-density. In this work, an attempt has been made to reduce the effect of power-density along with area and power so that AND-XOR based circuit is balanced in terms of area, power, and temperature. AND-XOR based reed-muller (RM) mixed polarity circuit forms are considered in this work. Polarity conversions are made in such a way that possibility of maximum sharing among the sub-function is increased. Genetic algorithm is (a non-exhaustive heuristic algorithm) used to select the polarity of the input variable for maximum sharing. The proposed synthesis approach shows 27.11%, 20.69%, and 32.30% savings in area, power, and power-density respectively than that of reported results. For the validation of the proposed approach, the best solutions are implemented in Cadence digital domain to obtain actual silicon area and power consumption. HotSpot tool is used to get the absolute temperature of the circuit.

The multi-robot cooperative planning is gained significant attention in recent past mainly for the evaders hunting task. In evaders hunting, the robot nodes required to recognize their other team members and considering their current positions and capabilities to catch the stationary or moving evaders effectively through the cooperating path planning approach. The primary challenge to design cooperative multi-robot evader hunting system is efficient and adaptive coordination of multiple autonomous mobile robots with less delay and communication overhead in presence of big-size obstacles. The current solutions suffered from repeated hunting problem under the inaccessible network conditions due to the presence of big-size obstacles and ineffective utilization of known nodes information. In this paper, to alleviate the problem of repeated hunting and inefficient catching of all evaders in the network, we proposed the adaptive Bio-inspired Neural Network (ABNN) using the new shunting equation with the capability of adaptive hunting of all evaders in the system. We design ABNN based on the implicit robot to predict the next path to catch evaders efficiently by real robots. The use of implicit robot helps to prevent the big sized evaders and efficiently utilize the evader’s information. The simulation results demonstrate that ABNN performs efficient evaders hunting under the presence of big size obstacles.

Multicore processing is advantageous over single core processors in the present highly advanced time critical applications. The tasks in real time applications need to be completed within the prescribed deadlines. Based on this philosophy, the proposed paper discusses the concept of load balancing algorithms in such a way that the work load is equally distributed amongst all cores in the processor. The equal distribution of work load amongst all the cores will result in enhanced utilization and increase in computing speed of application with all the deadlines met. In the heuristic based load balanced algorithm (HBLB), the best task from the set of tasks is selected using the feasibility check window and is assigned to the core. The application of HBLB reduces imbalance among the cores and results in lesser migration leading to low migration overhead. By utilizing all the cores of the multicore system, the computing speed of the application increases tremendously which results in the increase in efficiency of the system. The present paper also discusses the improved version of HBLB, known as Improved_Heuristic Based Load Balancing (Improved_HBLB), which focuses on further reducing the imbalance and the number of backtracks as compared to HBLB algorithm. It was observed that Improved_HBLB gives approximately 10% better results over the HBLB algorithm.

Telemonitoring in the field of healthcare has vastly improved the quality of clinical diagnosis and disease prevention by providing timely medical consultation to people living in rural and remote areas. To monitor the health state of a patient certain vital physiological parameter like electrocardiogram (ECG), respiration rate, blood pressure, oxygen saturation, etc. are acquired and analyzed. Listening to the heart sounds (auscultation) is also a quick method to monitor the health state of the patient’s heart. In this paper, we propose the use of a portable Doppler ultrasound sensor for measuring the heart sounds reliably and to transmit the data for further clinical telemonitoring. We have developed an ultrasound-based hardware prototype which is non-invasive in nature and easy to operate. Its portability, high accuracy, low cost, and wireless nature make this device suitable for home-based self-diagnostic applications. The developed prototype was successfully able to capture both fundamental heart sounds S1 and S2 reliably and transfer the signal wirelessly to the LabVIEW-based monitoring and data logging unit. This unit extracts clinically useful health information like heart rate (HR), R-R interval and heart rate variability (HRV) using signal processing algorithms. Health information is then transmitted via the Internet to a distant hospital for further improved clinical diagnosis and consultancy. The prototype was validated on 40 healthy males in the age group of 25-35 years, and the results show an overall accuracy of 96.74% in HR detection when compared with an ECG sensor, a photoplethysmograph (PPG) sensor, a pulse oximeter device and manual auscultation.

In recent days, component-based software engineering has become popular in the software industry for its reuse property. A suitable component-based software model is crucial for the effective design of the component-based software engineering. Quality assessment, evaluation, and analysis of a component model are highly essential to maintain the efficient design in the development of such system. Quality measurement for the component model will be more accurate, if it can be measured by a set of valid and meaningful metrics. This paper has proposed an empirical approach to validate a set of quality metrics along with a set of quality attributes for the design model of component-based software. In the proposed approach, metrics interdependencies have described using a Chi-Square non-parametric test. This paper has considered six different case studies of a well-known library management system to establish the metrics interdependency along with several quality attributes of a component model. This helps to identify the practically useful set of metrics for the quality assessment of high cohesive and low coupling metrics of the component-based system. A massive dataset has been collected from the 34 students of the institute on these six case studies. The Pearson's correlation method has been applied on the collected data set to identify the several correlations between the set of metrics and the set of quality attributes in terms of operation time. This facilitates to assess different crucial quality attributes of component-based system (CBS) design like complexity, analyzability, expressiveness etc.