(CDRGI)-Cancer detection through relevant genes identification

Cancer is a genetic disease that is categorized among the most lethal and belligerent diseases. An early staging of the disease can reduce the high mortality rate associated with cancer. The advancement in high throughput sequencing technology and the implementation of several Machine Learning algorithms have led to significant progress in Oncogenomics over the past few decades. Oncogenomics uses RNA sequencing and gene expression profiling for the identification of cancer-related genes. The high dimensionality of RNA sequencing data makes it a complex and large-scale optimization problem. CDRGI presents a Discrete Filtering technique based on a Binary Artificial Bee Colony coupling Support Vector Machine and a two-stage cascading classifier to identify relevant genes and detect cancer using RNA seq data. The proposed approach has been tested for seven different cancers, including Breast Cancer, Stomach Cancer (STAD), Colon Cancer (COAD), Liver Cancer, Lung Cancer (LUSC), Kidney Cancer (KIRC), and Skin Cancer. The results revealed that the CDRGI performs better for feature reduction while achieving better classification accuracy for STAD, COAD, LUSC and KIRC cancer types

Parallel tensor factorization for relational learning

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL

COVID-19 Patient Count Prediction Using LSTM

IEEE In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients\u27 estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model\u27s prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients\u27 count of the proposed model is much closer to the actual patient count

Structured knowledge creation for Urdu language: A DBpedia approach

Wikipedia information is extracted by DBpedia and linked to other web resources as Linked Open Data, which is an important contribution to the field of semantics. As part of its internationalisation endeavour, DBpedia now has 20 language chapters that have been mapped to it; nonetheless, there have been very few attempts from Urdu. This article outlines the procedures and highlights the efforts put forward as the first contribution to the manual creation of Urdu mappings with DBpedia Ontology classes. Our approach led to an increase in the number of mapped infoboxes, thus enhancing the DBpedia. The mapping procedure is broken down into two parts. The infobox template is first mapped to the DBpedia ontology's relevant class, and then the attributes of the infobox are mapped to the properties of that class. In addition, alongside other mapped languages, Urdu labels are included to the description of Ontology classes. We have covered around a thousand properties and attributes of Urdu with English DBpedia Ontology on DBpedia mapping server

Box–Behnken Response Surface Design of Polysaccharide Extraction from Rhododendron arboreum and the Evaluation of Its Antioxidant Potential

© 2020 by the authors. In the present investigation, the ultrasound-assisted extraction (UAE) conditions and optimization of Rhododendron arboreum polysaccharide (RAP) yield were studied by a Box–Behnken response surface design and the evaluation of its antioxidant potential. Three parameters that affect the productivity of UAE, such as extraction temperature (50–90 ◦C), extraction time (10–30 min), and solid–liquid ratio (1–2 g/mL), were examined to optimize the yield of the polysaccharide percentage. The chromatographic analysis revealed that the composition of monosaccharides was found to be glucose, galactose, mannose, arabinose, and fucose. The data were fitted to polynomial response models, applying multiple regression analysis with a high coefficient of determination value (R2 = 0.999). The data exhibited that the extraction parameters have significant effects on the extraction yield of polysaccharide percentage. Derringer’s desirability prediction tool was attained under the optimal extraction conditions (extraction temperature 66.75 ◦C, extraction time 19.72 min, and liquid–solid ratio 1.66 mL/g) with a desirability value of 1 yielded the highest polysaccharide percentage (11.56%), which was confirmed through validation experiments. An average of 11.09 ± 1.65% of polysaccharide yield was obtained in optimized extraction conditions with a 95.43% validity. The in vitro antioxidant effect of polysaccharides of R. arboreum was studied. The results showed that the RAP extract exhibited a strong potential against free radical damage

An Assortment of Evolutionary Computation Techniques (AECT) in gaming

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Real-time strategy (RTS) games differ as they persist in varying scenarios and states. These games enable an integrated correspondence of non-player characters (NPCs) to appear as an autodidact in a dynamic environment, thereby resulting in a combined attack of NPCs on human-controlled character (HCC) with maximal damage. This research aims to empower NPCs with intelligent traits. Therefore, we instigate an assortment of ant colony optimization (ACO) with genetic algorithm (GA)-based approach to first-person shooter (FPS) game, i.e., Zombies Redemption (ZR). Eminent NPCs with best-fit genes are elected to spawn NPCs over generations and game levels as yielded by GA. Moreover, NPCs empower ACO to elect an optimal path with diverse incentives and less likelihood of getting shot. The proposed technique ZR is novel as it integrates ACO and GA in FPS games where NPC will use ACO to exploit and optimize its current strategy. GA will be used to share and explore strategy among NPCs. Moreover, it involves an elaboration of the mechanism of evolution through parameter utilization and updation over the generations. ZR is played by 450 players with varying levels having the evolving traits of NPCs and environmental constraints in order to accumulate experimental results. Results revealed improvement in NPCs performance as the game proceeds

Modified cat swarm optimization for clustering

Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique

Small-scale production of hydrogen via auto-thermal reforming in an adiabatic packed bed reactor:Parametric study and reactor's optimization through response surface methodology

In this work, a two-dimensional (2-D) heterogeneous reactor model for ATR process is presented. In order to authenticate the developed reactor model outputs, literature results as well as thermodynamic findings produced by employing chemical equilibrium with applications (CEA) software were compared with the model predictions and an excellent agreement was evidenced that corroborates the model's accurate predictive capability. Response surface methodology combined with central composite design was used to investigate the significance of operational parameters on the performance of the ATR process and Parametric optimization was performed to find the optimal operating conditions. Further insights into the ATR process were obtained by studying the effect of temperature, pressure, S/C, oxygen to carbon ratio (O/C) and gas mass flow velocity (Gs) on CH4 conversion, H2 yield (wt. % of CH4) and H2 purity. It was concluded that 973 K, 1.5 bar, S/C of 3.0, O/C of 0.45 and Gs of 0.15 kg/m2s resulted in CH4 conversion and H2 purity up to 97.6% and 71.8%, respectively