Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication

Abstract: In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users’ fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep $Q$ -network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.

Novel neutrality operation–based Pythagorean fuzzy geometric aggregation operators for multiple attribute group decision analysis

Abstract: Pythagorean fuzzy sets (PFSs) accommodate more uncertainties than Lx the intuitionistic fuzzy sets and hence its applications are more extensive. Under the PFS, the objective of this paper is to develop some new operational laws and their corresponding weighted geometric aggregation operators. For it, we define some new neutral multiplication and power operational laws by including the feature of the probability sum and the interaction coefficient into the analysis to get a neutral or a fair treatment to the membership and nonmembership functions of PFSs. Associated with these operational laws, we define some novel Pythagorean fuzzy weighted, ordered weighted, and hybrid neutral geometric operators for Pythagorean fuzzy information, which can neutrally treat the membership and nonmembership degrees. The desirable relations and the characteristics of the proposed operators are studied in details. Furthermore, a multiple attribute group decision‐making approach based on the proposed operators under the Pythagorean fuzzy environment is developed. Finally, an illustrative example is provided to show the practicality and the feasibility of the developed approach.

Choosing Best Hashing Strategies and Hash Functions

Abstract: The paper gives the guideline to choose a best suitable hashing method hash function for a particular problem. After studying the various problem we find some criteria has been found to predict the best hash method and hash function for that problem. We present six suitable various classes of hash functions in which most of the problems can find their solution. Paper discusses about hashing and its various components which are involved in hashing and states the need of using hashing for faster data retrieval. Hashing methods were used in many different applications of computer science discipline. These applications are spread from spell checker, database management applications, symbol tables generated by loaders, assembler, and compilers. There are various forms of hashing that are used in different problems of hashing like Dynamic hashing, Cryptographic hashing, Geometric hashing, Robust hashing, Bloom hash, String hashing. At the end we conclude which type of hash function is suitable for which kind of problem.

Reliability, Availability and Maintainability Analysis of Industrial Systems Using PSO and Fuzzy Methodology

Abstract: The purpose of this paper is to present a methodology for analyzing the system performance of an industrial system by utilizing uncertain data. Although there have been tremendous advances in the art and science of system evaluation, yet it is very difficult to assess their performance with a very high accuracy or precision. For handling of these uncertainties, fuzzy set theory has been used in the analysis while their corresponding membership functions are generated by solving a nonlinear optimization problem with particle swarm optimization. For finding the critical component of the system which affects the system performance mostly, a composite measure of reliability, availability and maintainability (RAM) named as the RAM-index has been introduced which influences the effects of failure and repair rate parameters on its performance. A time varying failure and repair rate parameters are used in the analysis instead of constant rate models. Finally, the computed results are finally compared with existing methodologies. The suggested framework has been illustrated with the help of a case.

Generalized Intuitionistic Fuzzy Entropy-Based Approach for Solving Multi-attribute Decision-Making Problems with Unknown Attribute Weights

Abstract: A new strategy for solving multi-attribute decision-making problem has been presented by using different entropies and unknown attribute weights, where preferences related to the attributes are in the form of interval-valued intuitionistic fuzzy sets. Some generalized properties have also been proved for justification. An illustrative example has been provided to demonstrate and effectiveness the approach along with the sensitivity analysis on the decision-maker parameter.