This paper presents the application of gaining sharing knowledge (GSK) based optimization algorithm to an uncertain solid transportation (UST) problem. The UST problem consists of supply, demand, and conveyance constraints under uncertain environment. To solve the said problem, the expected criterion model is considered so that the expected value of the objective function is minimized. 99-method generates the expected value of the assumed uncertain variables, and the transformed problem is solved. Due to the consideration of integer decision variables, GSK is modified to integer gaining

Energy efficiency (EE) is one of the main parameters to be considered in recent networks targeting green technology. Our system is based on cloud radio access networks and it consists of a macro base-station and many small remote radio heads (RRHs). We solve an optimization problem to improve the system's EE through resource allocation and power control. We also reduce the power consumption through switching the RRHs ON/OFF based on the current users' distribution. We formulate the problem as EE maximization with constraints to provide full frequency reuse between RRHs. Our solution divides

Parkinson's disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients' voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival Tp slots ahead, where Tp denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted

In this paper, we address the problem of distributed interference management of femtocells that share the same frequency band with macrocells using distributed multi-agent Q-learning. We formulate and solve two problems representing two different Q-learning algorithms, namely, femto-based distributed and sub-carrier-based distributed power controls using Q-learning (FBDPC-Q and SBDPC-Q). FBDPC-Q is a multi-agent algorithm that works on a global basis, for example, deals with the aggregate macrocell and femtocell capacities. Its complexity increases exponentially with the number of sub-carriers

In this paper, we study the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs) under quality of service (QoS) constraints. The SUs network is controlled by the PU through a relay which gets a revenue for amplifying and forwarding the SUs' signals to their respective destinations. The relay charges each SU a different price depending on its received signal-to-interference-and-noise ratio (SINR). The primary relay controls the SUs network and maximize any desired PU utility function. The PU utility function represents its QoS, which is affected

As a powerful optimization algorithm for solving nonlinear, complex and tough global optimization problems, differential evolution (DE) has been widely applied in various science and engineering fields. In this article, considering that the evolution direction of each individual is not fully exploited to guide the search process in most DE algorithms, a new DE variant ( named ADEwSE), which incorporates the successful experience of evolved individuals into classic ''current-to-pbest/1'' mutation strategy to reduce the randomness of search direction, is proposed. Moreover, crossover matrix

Device-free passive (DfP) localization has been recently proposed to allow localizing a stationary entity that neither carries a device nor participates actively in the localization process. In this paper, we present a Kalman filter-based system that enables tracking a continuously moving entity in a typical wireless environment rich in multipath. The concept behind DfP tracking is that the received signal strength at monitoring points in a wireless environment is influenced by any changes in the environment. These changes include the movement of an entity, such as a human being, within the

In this paper, we introduce the novel concept of cost-effective mobile health care which leverages the multiple wireless interfaces onboard most mobile phones today. First, we study the problem of uploading medical data using the 'least cost' radio interface. Toward this objective, we propose the wireless interface selection algorithm (WISA) which decides the wireless interface yielding the least cost, depending on the data size, modality, and quality of service (QoS). Second, we study using modeling and simulations, the problem of cost-effective medical advisory message dissemination (on the

Massive numbers of Internet of Things (IoT) connections represent an essential component of the next-generation wireless networks. However, catering for such unprecedented numbers via cellular networks significantly increases the network congestion and degrades the achievable quality of service (QoS). Hence, traffic offloading has been proposed to alleviate the expected high growth rate in cellular networks. It exploits several network techniques such as WiFi networks, device to device (D2D) communication and heterogeneous networks (HetNets) to deliver user data primarily determined for the