Hybrid multi-verse optimizer with grey wolf optimizer for power scheduling problem in smart home using IoT

The Journal of Supercomputing - The power scheduling problem in smart home (PSPSH) is one of the complex NP-hard scheduling problems, where it has a deep and rugged search space due to the high...     

An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

Neural Computing and Applications - The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization...     

Utilization of Raw and Activated Date Pits for the Removal of Phenol from Aqueous Solutions

Activated carbons prepared from date pits, an agricultural waste byproduct, have been examined for the adsorption of phenol from aqueous solutions. The activated carbons were prepared using a fluidized bed reactor in two steps; carbonization at 700 °C for 2 hours in N₂ atmosphere and activation at 900 °C in CO₂ atmosphere. The kinetic data were fitted to the models of intraparticle diffusion, pseudo‐second order, and Lagergren, and followed more closely the pseudo‐second‐order chemisorption model. The isotherm equilibrium data were well fitted by the Freundlich and Langmuir models. The maximum adsorption capacity of activated date pits per Langmuir model was 16 times higher than that of nonactivated date pits. The thermodynamic properties calculated revealed the endothermic nature of the adsorption process. The uptake of phenol increased with increasing initial phenol concentration from10 to 200 ppm and temperature from 25 to 55 °C, and decreased with increasing the solution pH from 4 to 12. The uptake of phenol was not affected by the presence of NaCl salt.     

Engineering molecular communications integrated with carbon nanotubes in neural sensor nanonetworks

There have been recent advances in the engineering of molecular communication (MC)‐based networks for nanomedical applications. However, the integration of MC with biomaterials such as carbon nanotubes (CNTs) presents various critical research challenges. In this study, the authors envisaged integrating MC‐based nanonetwork with CNTs to optimise nanonetwork performance. In neural networks, a chronic reduction in the concentration of the neurotransmitter acetylcholine (ACh) eventually leads to the development of neurodegenerative diseases; therefore, they used CNTs as a molecular switch to optimise ACh conductivity supported by artificial MC. Furthermore, MC enables communication between transmitter neurons and receiver neurons for fine‐tuning the ACh release rate according to the feedback concentration of ACh. Subsequently, they proposed a min/max feedback scheme to fine‐tune the expected throughput and ACh transmission efficiency. For demonstration purposes, they deduced analytical forms for the proposed schemes in terms of throughput, incurred traffic rates, and average packet delay.Inspec keywords: carbon nanotubes, cellular biophysics, diseases, feedback, nanomedicine, nanosensors, neural nets, neurophysiologyOther keywords: carbon nanotubes, neural sensor nanonetworks, nanomedical applications, biomaterials, molecular communication‐based nanonetwork, neural networks, neurotransmitter acetylcholine, neurodegenerative diseases, transmitter neurons, receiver neurons     

Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its modified search equation that depends on solutions of mean previous and global best is used. Furthermore, to solve the main issues of FCM, Automatic clustering algorithm was proposed based on the mean artificial bee colony called (AC-MeanABC). It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value. A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC. The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.     

A novel ensemble statistical topic extraction method for scientific publications based on optimization clustering

Multimedia Tools and Applications - The automatic topic extraction (TE) from scientific publications provides a very compact summary of the clusters’ contents. This often helps in locating...     

Recent advances of bat-inspired algorithm,its versions and applications

Neural Computing and Applications - Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main...     

Heavy metals in vegetables sold in the local market in Jordan

Heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn) in various vegetables (cabbage, green onion, lettuce, parsley, rocket, spinach, carrot, onion, potato and cauliflower) from the market in Jordan were measured using inductively coupled plasma-mass spectrometry. As, Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn ranged from 0.009–0.275 mg kg^?1 wet weight, 0.004–0.060 mg kg^?1, 0.003–0.401 mg kg^?1, 0.105–3.51 mg kg^?1, 0.15–1.15 mg kg^?1, 0.93–14.39 mg kg^?1, 0.044–0.702 mg kg^?1, 0.072–0.289 mg kg^?1 and 2.23–6.65 mg kg^?1, respectively. Parsley, followed by spinach, contained the highest concentration of heavy metals. Onion contained high levels of toxic heavy metals. The content of Cu in parsley and spinach and Pb in onion exceeded the Codex limits. However, the daily intake of heavy metals from the tested vegetables was lower than the maximum limits for allowable intake.