Android CompCache Based on Graphics Processing Unit

Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low memory killer. These memory management methods are fully functioning. However, Android phones cannot swap memory into solid-state drives, thus slowing the processor and reducing storage lifetime. In addition, the compression and decompression processes consume additional energy and latency. Therefore, the CompCache requires an extension. An extended Android CompCache using a graphics processing unit to compress and decompress memory pages on demand and reduce the latency is introduced in this paper. This paper characterizes each data compression and decompression utility by measuring compression ratio, compression and decompression throughput, and energy efficiency to validate the process. Experimental results prove that data compression and decompression utilities can be beneficial to reduce the latency and perform faster compression and decompression compared with existing approache

Gene set imputation method-based rule for recovering missing data using deep learning approach

Data imputation enhances dataset completeness, enabling accurate analysis and informed decision-making across various domains. In this research, we propose a novel imputation method, a spectral clustering based on a gene set using adaptive weighted k-nearest neighbor (AWKNN), and an imputation of missing data using a convolutional neural network algorithm for accurate imputed data. In this research, we have considered the Kaggle water quality dataset for the imputation of missing values in water quality monitoring. Data cleaning detects inaccurate data from the dataset by using the median modified Weiner filter (MMWFILT). The normalization technique is based on the Z-score normalization (Z-SN) approach, which improves data organization and management for accurate imputation. Data reduction minimizes unwanted data and the amount of capacity required to store data using an improved kernel correlation filter (IKCF). The characteristics and patterns of data with specific columns are analyzed using enhanced principal component analysis (EPCA) to reduce overfitting. The dataset is classified into complete data and missing data using the light- DenseNet (LIGHT DN) approach. Results show the proposed outperforms traditional techniques in recovering missing data while preserving data distribution. Evaluation based on pH concentration, chloramine concentration, sulfate concentration, water level, and accuracy

A hierarchical classifier for multiclass prostate histopathology image gleason grading

Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4).To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and one- versus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed.However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the ‘divide-and-conquer’ principle by allocating each binary task into two separate subtasks; strong and weak, based on the power of the samples in each binary task. Conversely, the strong samples include more information about the considered task, which motivates the production of the final prediction. Experimental results on prostate histopathological images illustrated that the MLA significantly outperforms the Ovall and OVO approaches when applied to the ensemble framework.The results also confirmed the high efficiency of the ensemble framework with the MLA scheme in dealing with the multiclass classification problem

A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions