Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework.

The timely identification of autism spectrum disorder (ASD) in children is imperative to prevent potential challenges as they grow. When sharing data related to autism for an accurate diagnosis, safeguarding its security and privacy is a paramount concern to fend off unauthorized access, modification, or theft during transmission. Researchers have devised diverse security and privacy models or frameworks, most of which often leverage proprietary algorithms or adapt existing ones to address data leakage.

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis.

The article discusses the need for a lightweight software architecture evaluation framework that can address practitioners' concerns. Specifically, the proposed framework uses process mining and Petri nets to analyze security and performance in software development's early and late stages. Moreover, the framework has been implemented in six case studies, and the results show that it is a feasible and effective solution that can detect security and performance issues in complex and heterogeneous architecture with less time and effort.

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations.

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems.

Forest Fire Detection Using New Routing Protocol.

The Mobile Ad-Hoc Network (MANET) has received significant interest from researchers for several applications. In spite of developing and proposing numerous routing protocols for MANET, there are still routing protocols that are too inefficient in terms of sending data and energy consumption, which limits the lifetime of the network for forest fire monitoring. Therefore, this paper presents the development of a Location Aided Routing (LAR) protocol in forest fire detection.

Lightweight Software Architecture Evaluation for Industry: A Comprehensive Review.

Processes for evaluating software architecture (SA) help to investigate problems and potential risks in SA. It is derived from many studies that proposed a plethora of systematic SA evaluation methods, while industrial practitioners currently refrain from applying them since they are heavyweight. Nowadays, heterogeneous software architectures are organized based on the new infrastructure.

Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.

Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN.

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases.

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated.

Framework for Parallel Preprocessing of Microarray Data Using Hadoop.

Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises.

Converting differential-equation models of biological systems to membrane computing.

This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally.

Evaluating ligand-receptor networks of TGF-beta with membrane computing.

Ligand-Receptor Networks of TGF-beta plays essential role in transmitting a wide range of extracellular signals that affect many cellular processes such as cell growth. However, the modeling of these networks with conventional approach such as ordinary differential equations has not taken into account, the spatial structure and stochastic behavior of processes involve in these networks. Membrane computing as the alternatives approach provides spatial structure for molecular computation in which processes are evaluated in a non-deterministic and maximally parallel way.