Improved feature selection using a hybrid side-blotched lizard algorithm and genetic algorithm approach

Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of side-blotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient

An Integrated Framework for Delivering Healthcare Services through Information Technology

Egyptian integrated healthcare services delivery is undergoing a transformative shift driven by the rapid advancements in information technology . This paper presents an integrated framework designed to leverage IT solutions for the efficient delivery of healthcare services for patients. The proposed framework encompasses various components, including electronic health records , telemedicine, and interoperability standards.The integration of electronic health records serves as the foundation for seamless information exchange among healthcare providers, ensuring a comprehensive and up-to-date view of patient health. Telemedicine, a key aspect of the framework, facilitates remote consultations, enabling healthcare professionals to extend their reach and provide timely interventions. Additionally, the framework incorporates advanced data analytics techniques to derive meaningful insights from the vast amount of healthcare data generated

Project Scheduling: Survey and Research Potentials

Abstract: project scheduling is very critical topic in project management. Resource constrained project scheduling problem (RCPSP) consists of activities that must be scheduled based on dependencies relationships and priorities of activities. In the recent years there have been many survey papers around the area of project scheduling, as many researchers developed both exact and heuristic scheduling schemes. This paper give an over view around the resource constrained project scheduling problem (RCPSP)

Statistical Modeling of the Effects of Process Variations on Silicon Photonics

The rapidly growing field of silicon photonics is an attractive research and manufacturing platform, due to its ability to enable novel functionalities. Silicon photonics leverages existing CMOS processes and fabrication infrastructure, making its components suffer from the process variations present in CMOS technology. Long and repetitive simulations are required to understand the effect of these variations, largely due to the lack of variation-aware models. This thesis explores methodologies for the development and application of process variation-aware compact models for silicon photonics components to enable photonics design for manufacturability. We consider the effect of a number of common unavoidable process variations, including both systematic and random variations, on the behavior of key optical building blocks. We examine the effect of line edge roughness as a random process variation on different components including Y-branches and coupled resonator optical waveguides. For the Y-branch, we use ensemble simulations to develop behavioral statistical models that can predict the behavior in the presence of different line edge roughness parameters. In the case of coupled resonator optical waveguides, to predict the behavior in the presence of different line edge roughness parameters, we develop an S-parameter based model that can be used directly in circuit simulation. Also, we present methods to develop S-parameter based compact models against systematic variations (geometric variations) in rings for both silicon and silicon nitride waveguides. The models are capable of predicting the behavior much faster than by full wave simulations, and give insight on resulting performance variation to enable yield prediction and optimization. We use the developed compact model to simulate photonic integrated circuits and compare the time required with the case of traditional simulations loops. We also present methods for extraction of spatial variations using variation test chip design and measurement. The spatial variations are decomposed into die-to-die and within-die variations. We examine modulation (electrical and thermal) as a conventional approach to account for the effect of process variations. For electrical modulation, we study typical operating condition variations it can experience and find that their effect is not as severe as typical process variations. Moreover, the power budget required to correct for process variations is calculated. Together, these methods are key components toward design for manufacturability approaches and serve as a basis for extended PDKs for silicon photonics. Such models and methods help increase the speed of the simulation process required in photonics integrated circuit design, and inform designers of potential design modifications to correct for process variations for high yield and performance.Ph.D

Integrated Framework of Optimization Technique and Information Theory Measures for Modeling Neutrosophic Variables

Uncertainty and indeterminacy are two major problems in data analysis these days. Neutrosophy is a generalization of the fuzzy theory. Neutrosophic system is based on indeterminism and falsity of concepts in addition to truth degrees. Any neutrosophy variable or concept is defined by membership, indeterminacy and non-membership functions. Finding efficient and accurate definition for neutrosophic variables is a challenging process. This paper presents a framework of Ant Colony Optimization and entropy theory to define a neutrosophic variable from concrete data

A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems

The multi-objective integer programming problems are considered time consuming. In the past, mathematical structures were used that can get benefits of high processing powers and parallel processing. A general approach to generate all non-dominated solutions of the multiobjective integer programming (MOIP) Problem is developed. In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented for solving integer multi-objective problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the MOIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization