Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2021

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 1351 - 1380 of 3477

Full-Text Articles in Computer Sciences

Energy Management Of Marine Current Power Generation System Based On Fuzzy Logical Control, Jingang Han, Li Xu Jun 2021

Energy Management Of Marine Current Power Generation System Based On Fuzzy Logical Control, Jingang Han, Li Xu

Journal of System Simulation

Abstract: The fluctuation of marine current velocity leads to the fluctuation of power generation. The hybrid energy storage system composed of vanadium redox flow battery and super capacitor is used to improve the power quality and smooth the marine current power generation. With the research and commercialization of large scale power marine current power generation system, the capacity of energy storage system has been increasing. To reduce the rated capacity of super capacitor banks in energy storage system, an energy management strategy based on low-pass filter optimization algorithm of fuzzy control is proposed and the simulation model of 3 MW …

Go to article

Development Of Semi-Physical Simulation Platform For Monitoring Municipal Solid Waste Incineration Process, Aijun Yan, Xia Heng, Xizhi Liu Jun 2021

Development Of Semi-Physical Simulation Platform For Monitoring Municipal Solid Waste Incineration Process, Aijun Yan, Xia Heng, Xizhi Liu

Journal of System Simulation

Abstract: In order to research and test the modeling, control and optimization of MSW (Municipal Solid Waste) incineration process, a semi-physical simulation platform with three-layer structure by combining the physical control system with the virtual object is developed. The physical control system of the platform is composed of an intelligent control optimization layer and basic control layer; and the virtual object layer includes simulated instrument, actuator and incineration process model. The software for man-machine interface with OPC (Object Linking and Embedding (OLE) for Process Control), equipment/parameter monitoring, and incineration process model are developed. The functions of intelligent control optimization layer …

Go to article

Development Paths Of New Energy Vehicles Incorporating Co2 Emissions Trading Scheme, Wenxiang Li, Li Ye, Jieshuang Dong, Yiming Li Jun 2021

Development Paths Of New Energy Vehicles Incorporating Co2 Emissions Trading Scheme, Wenxiang Li, Li Ye, Jieshuang Dong, Yiming Li

Journal of System Simulation

Abstract: New energy vehicles in China have entered the “post-subsidy era”, and it is urgent to explore and establish a long-term mechanism for market-oriented development. The CO2 emission trading scheme for road transport (ETS-RT) is introduced to replace financial subsidies, forming a market-oriented incentive and punishment mechanism. Therefore, a market mechanism is established where internal combustion engine vehicles feed new energy vehicles. A causal loop diagram of system dynamics was used to analyze the key policy parameters of the ETS-RT that affect the development of new energy vehicles. Then a multi-agents-based model of ETS-RT is established to simulate the …

Go to article

Review On Key Technologies Of Industrial Control System Security Simulation, Bailing Wang, Hongri Liu, Yaofang Zhang, Sicai Lü, Zibo Wang, Qimeng Wang Jun 2021

Review On Key Technologies Of Industrial Control System Security Simulation, Bailing Wang, Hongri Liu, Yaofang Zhang, Sicai Lü, Zibo Wang, Qimeng Wang

Journal of System Simulation

Abstract: In order to cope with the increasingly serious industrial internet security problems, simulation is viewed as a critical backboned technology for drilling of network attack and defense, tracking back and analyzing of security incidents, as well as validating of pre-research technology. On the basis of summarizing the studies of three types of typical industrial control system security simulation platform, the key technologies used in industrial control cyber range and industrial control honeynets are highlighted. Technologies are summarized, such as the virtualization technology of industrial control network and industrial control components, the construction technology of industrial control target field dominated …

Go to article

Universal Biological Motions For Educational Robot Theatre And Games, Rajesh Venkatachalapathy, Martin Zwick, Adam Slowik, Kai Brooks, Mikhail Mayers, Roman Minko, Tyler Hull, Bliss Brass, Marek Perkowski Jun 2021

Universal Biological Motions For Educational Robot Theatre And Games, Rajesh Venkatachalapathy, Martin Zwick, Adam Slowik, Kai Brooks, Mikhail Mayers, Roman Minko, Tyler Hull, Bliss Brass, Marek Perkowski

Complex Systems Faculty Publications and Presentations

Paper presents a concept that is new to robotics education and social robotics. It is based on theatrical games, in motions for social robots and animatronic robots. Presented here motion model is based on Drift Differential Model from biology and Fokker-Planck equations. This model is used in various areas of science to describe many types of motion. The model was successfully verified on various simulated mobile robots and a motion game of three robots called "Mouse and Cheese."

Go to article

Analyzing Public Sentiment On Covid-19 Pandemic, Pradeepika Gedupudi Jun 2021

Analyzing Public Sentiment On Covid-19 Pandemic, Pradeepika Gedupudi

Master's Projects

Sentiment analysis is a method of understanding the user sentiment expressed in the form of text. Social media is the best place to capture the public's opinion regarding how they feel about current events. The Corona Virus Disease-2019 (COVID-19) is one of the worst pandemics we have experienced so far. An important observation is that this pandemic has not only affected the public's physical health but also took a toll on their mental health. Reddit is a social news discussion site where people discuss topics around current affairs in smaller groups called subreddits. The project's primary focus is to build …

Go to article

Improving The Security And Performance Of Web Applications Running On The Distributed Ipfs, Vu Le Jun 2021

Improving The Security And Performance Of Web Applications Running On The Distributed Ipfs, Vu Le

Master's Projects

While cloud computing is gaining widespread adoption these days, some challenges are emerging around security, performance, and reliability of centralized cloud resources. Decentralized services are introduced as an effective way to overcome the limitations of cloud services. Blockchain technology with its associated decentralization is used to develop decentralized application platforms. The interplanetary file system (IPFS) is built on top of a distributed system consisting of a group of nodes that shares the data and also takes advantage of blockchain to permanently store the data. The IPFS is very useful in transferring data between people. This project focuses on blockchain technology, …

Go to article

Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali Jun 2021

Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali

All Works

The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name a few. This evolution of the smartphone community has opened new horizons for malware developers. As malware variants are growing at a tremendous rate every year, there is an urgent need to combat against stealth malware techniques. This paper proposes a visualization and machine learning-based framework for classifying Android …

Go to article

Improving Facial Emotion Recognition With Image Processing And Deep Learning, Ksheeraj Sai Vepuri Jun 2021

Improving Facial Emotion Recognition With Image Processing And Deep Learning, Ksheeraj Sai Vepuri

Master's Projects

Humans often use facial expressions along with words in order to communicate effectively. There has been extensive study of how we can classify facial emotion with computer vision methodologies. These have had varying levels of success given challenges and the limitations of databases, such as static data or facial capture in non-real environments. Given this, we believe that new preprocessing techniques are required to improve the accuracy of facial detection models. In this paper, we propose a new yet simple method for facial expression recognition that enhances accuracy. We conducted our experiments on the FER-2013 dataset that contains static facial …

Go to article

Task Classification During Visual Search With Deep Learning Neural Networks And Machine Learning Methods, Siddartha Thentu Jun 2021

Task Classification During Visual Search With Deep Learning Neural Networks And Machine Learning Methods, Siddartha Thentu

Master's Projects

Studies have shown the possibility to classify user tasks from eye-movement data. We present a new way to determine the optimal model for different visual attention tasks using data that includes two types of visual search tasks, a visual exploration task, a blank screen task, and a task where a user needs to fixate at the center of any scene. We used deep learning and SVM models on RGB images generated from fixation scan paths from these tasks. We also used AdaBoost on filtered eye movement data as a baseline. Our study shows that deep learning gives the best accuracy …

Go to article

Who Uses Multi-Factor Authentication?, Leah Roberts Jun 2021

Who Uses Multi-Factor Authentication?, Leah Roberts

Undergraduate Honors Theses

A sample of 47 BYU students were recruited to participate in this study to determine who was using Multi-factor Authentication (MFA) on their online accounts. This study determined that there were many different factors that separated those who used MFA and those who did not. Some of those factors included: time spent on the internet each day, gender, the website itself, and personal privacy behaviors.

Go to article

Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli Jun 2021

Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli

Computer Science Faculty Research & Creative Works

The rise of on-demand healthcare and the unprecedented growth of electronic health records has given rise to big data opportunities and data analysis using machine learning. The massive and disparate data management using conventional databases is incredibly challenging and expensive to manage. It often requires specialized analytical tools for developing advanced data-driven capabilities and performing data analytics. This paper explores the capability of an open-source framework 'Apache Spark' capable of processing large amounts of data on clusters of nodes to analyze Big data and integrate technologies to provide decision support systems in healthcare settings. Next, we propose machine learning models …

Go to article

Adaptive Network Slicing In Fog Ran For Iot With Heterogeneous Latency And Computing Requirements: A Deep Reinforcement Learning Approach, Almuthanna Nassar Jun 2021

Adaptive Network Slicing In Fog Ran For Iot With Heterogeneous Latency And Computing Requirements: A Deep Reinforcement Learning Approach, Almuthanna Nassar

USF Tampa Graduate Theses and Dissertations

In view of the recent advances in Internet of Things (IoT) devices and the emerging new breed of smart city applications and intelligent vehicular systems driven by artificial intelligence, fog radio access network (F-RAN) has been recently introduced for the next generation wireless communications. The capability of F-RAN has emerged to overcome the latency limitations of cloud-RAN (C-RAN) and assure the quality-of-service (QoS) requirements of the ultra-reliable-low-latency-communication (URLLC) for IoT applications. To this end, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge. However, …

Go to article

Optimization And Machine Learning Methods For Solving Combinatorial Problems In Urban Transportation, Aigerim Bogyrbayeva Jun 2021

Optimization And Machine Learning Methods For Solving Combinatorial Problems In Urban Transportation, Aigerim Bogyrbayeva

USF Tampa Graduate Theses and Dissertations

This dissertation investigates three applications of emerging technologies for urban trans- portation. In the first chapter, we design a new market for fractional ownership of au- tonomous vehicles (AVs), in which an AV is co-leased by a group of individuals. We present a practical iterative auction based on the combinatorial clock auction to match the interested customers together and determine their payments. In designing such an auction, we con- sider continuous-time items (time slots) which are defined by bidders, and naturally exploit driverless mobility of AVs to form co-leasing groups. To relieve the computational burdens of both bidders and the …

Go to article

Characterizing And Optimizing Asynchronous Event-Driven Architecture For Modern Cloud Systems, Shungeng Zhang Jun 2021

Characterizing And Optimizing Asynchronous Event-Driven Architecture For Modern Cloud Systems, Shungeng Zhang

LSU Doctoral Dissertations

Achieving good performance and high efficiency simultaneously is an essential requirement for emerging modern cloud systems such as e-commerce due to their business impact. For example, Akamai reported that every 100ms delay in website load time could lead to a 6% drop in sales. Unfortunately, achieving good performance (e.g., low latency) for modern cloud systems at high resource utilization is significantly challenging. Despite continuing efforts by cloud professionals, however, they have consistently experienced performance degradation problems (e.g., the long-tail latency problem) due to the bursty workload in the cloud. To resolve the performance degradation problems, many previous research efforts have …

Go to article

Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney Jun 2021

Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney

Articles

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the …

Go to article

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead Jun 2021

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Go to article

Toward An Intelligent Driving Behavior Adjustment Based On Legal Personalized Policies Within The Context Of Connected Vehicles, Fatma Outay, Nafaa Jabeur, Hedi Haddad, Zied Bouyahia, Hana Gharrad Jun 2021

Toward An Intelligent Driving Behavior Adjustment Based On Legal Personalized Policies Within The Context Of Connected Vehicles, Fatma Outay, Nafaa Jabeur, Hedi Haddad, Zied Bouyahia, Hana Gharrad

All Works

The advent of Connected Vehicles (CVs) is creating new opportunities within the transportation sector. It is, indeed, expected to improve road traffic safety, enhance mobility, reduce fuel consumption and gas emissions, as well as foster economic growth via investments and jobs. However, to motivate the deployment of CVs and maximize their related benefits, policymakers must create appropriate neutral legal frameworks. These frameworks should promote the innovation of current road infrastructures, support cooperation and interoperability between transportation systems, and encourage fair competition between companies while upholding consumer privacy as well as data protection. We argue that policymakers should also support innovative …

Go to article

Affectivetda: Using Topological Data Analysis To Improve Analysis And Explainability In Affective Computing, Hamza Elhamdadi Jun 2021

Affectivetda: Using Topological Data Analysis To Improve Analysis And Explainability In Affective Computing, Hamza Elhamdadi

USF Tampa Graduate Theses and Dissertations

We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses conditional comparison of different emotions, both respective and irrespective of time, with multiple topological distance metrics, dimension reduction techniques, and face subsections (e.g., eyes, nose, mouth, etc.). The results confirm that our topology-based approach captures known patterns, distinctions between emotions, and distinctions between individuals, which is an important step towards more robust and explainable emotion recognition by machines.

Go to article

St-V-Net: Incorporating Shape Prior Into Convolutional Neural Networks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Weihua Zhou, Et. Al. Jun 2021

St-V-Net: Incorporating Shape Prior Into Convolutional Neural Networks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Weihua Zhou, Et. Al.

Michigan Tech Publications, Part 1

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the …

Go to article

Artificial Intelligence (Ai) And Augmented Reality (Ar): Disambiguated In The Telemedicine / Telehealth Sphere, Sharon L. Burton Jun 2021

Artificial Intelligence (Ai) And Augmented Reality (Ar): Disambiguated In The Telemedicine / Telehealth Sphere, Sharon L. Burton

Publications

The world is navigating through unfamiliar and incomprehensible times – COVID-19, international economic crisis, and crumbling healthcare systems. The United States (US) healthcare industry is grappling with an increased workload and advancing digitization technological concerns. The failure of organizations to offer suitable cybersecurity controls within the critical infrastructure leads to advanced persistent threat (APT) that could have incapacitating effects on organizations. A keen understanding of cybersecurity is vital for leaders and the need is referenced in US policy that advances a national unity of effort to strengthen and maintain secure, functioning, and resilient critical infrastructure. Akin to the Presidential Policy …

Go to article

St-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan-Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong-Wen Deng, Weihua Zhou Jun 2021

St-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan-Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong-Wen Deng, Weihua Zhou

Faculty Publications

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the …

Go to article

Automated Decision Making And Machine Learning: Regulatory Alternatives For Autonomous Settings, Alyssa Heminger Jun 2021

Automated Decision Making And Machine Learning: Regulatory Alternatives For Autonomous Settings, Alyssa Heminger

University Honors Theses

Given growing investment capital in research and development, accompanied by extensive literature on the subject by researchers in nearly every domain from civil engineering to legal studies, automated decision-support systems (ADM) are likely to see a place in the foreseeable future. Artificial intelligence (AI), as an automated system, can be defined as a broad range of computerized tasks designed to replicate human neural networks, store and organize large quantities of information, detect patterns, and make predictions with increasing accuracy and reliability. By itself, artificial intelligence is not quite science-fiction tropes (i.e. an uncontrollable existential threat to humanity) yet not without …

Go to article

Functional Role Of The N-Terminal Domain In Connexin 46/50 By In Silico Mutagenesis And Molecular Dynamics Simulation, Umair Khan Jun 2021

Functional Role Of The N-Terminal Domain In Connexin 46/50 By In Silico Mutagenesis And Molecular Dynamics Simulation, Umair Khan

University Honors Theses

Connexins form intercellular channels known as gap junctions that facilitate diverse physiological roles, from long-range electrical and chemical coupling to nutrient exchange. Recent structural studies on Cx46 and Cx50 have defined a novel and stable open state and implicated the amino-terminal (NT) domain as a major contributor to functional differences between connexin isoforms. This thesis presents two studies which use molecular dynamics simulations with these new structures to provide mechanistic insight into the function and behavior of the NTH in Cx46 and Cx50. In the first, residues in the NTH that differ between Cx46 and Cx50 are swapped between the …

Go to article

Case Study Of Scrum Methodology As Used By A Capstone Team, Lilly I. Yeaton Jun 2021

Case Study Of Scrum Methodology As Used By A Capstone Team, Lilly I. Yeaton

University Honors Theses

Scrum is widely used in the software industry to manage all kinds of projects. This case study examines the way in which a capstone team used the methodology and models the specific project management processes they used over the course of their project. These models and the process modifications therein are then compared to the team’s velocity at different points in the project. The results of this analysis suggest a correlation between asynchronous daily meetings and sprint reviews and improved velocity.

Go to article

Covid-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning, Yang Liu, You Wu, Xiaoke Shen, Lei Xie Jun 2021

Covid-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning, Yang Liu, You Wu, Xiaoke Shen, Lei Xie

Publications and Research

The life-threatening disease COVID-19 has inspired significant efforts to discover novel therapeutic agents through repurposing of existing drugs. Although multi-targeted (polypharmacological) therapies are recognized as the most efficient approach to system diseases such as COVID-19, computational multi-targeted compound screening has been limited by the scarcity of high-quality experimental data and difficulties in extracting information from molecules. This study introduces MolGNN , a new deep learning model for molecular property prediction. MolGNN applies a graph neural network to computational learning of chemical molecule embedding. Comparing to state-of-the-art approaches heavily relying on labeled experimental data, our method achieves equivalent or superior prediction …

Go to article

Characterizing The Vibrational Spectra Of Hydrogen Bonded Systems With Molecular Dynamics Simulations And Quantum Chemical Methods, Dalton Boutwell Jun 2021

Characterizing The Vibrational Spectra Of Hydrogen Bonded Systems With Molecular Dynamics Simulations And Quantum Chemical Methods, Dalton Boutwell

Master of Science in Chemical Sciences Theses

We apply and assess the utility of DMD for the purpose of investigating complex spectral features in N2H+···OC, N2D+···OC, C2O4H-, C2O4D- and (HCOOH)2. The proton transfer as a vibrational motion consists of diffuse qualities that can be accounted for with classical and quantum chemical analyses. Classical approaches yield a wealth of information about vibrational spectra at a reduced cost, as in the case of previously investigated N4H+. The isoelectronic N2H+···OC has …

Go to article

Data-Driven Artificial Intelligence For Calibration Of Hyperspectral Big Data, Vasit Sagan, Maitiniyazi Maimaitijiang, Sidike Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hartling, David Lebauer, Maria Newcomb, Duke Pauli, Kyle T. Peterson, Nadia Shakoor, Abby Stylianou, Charles S. Zender, Todd C. Mockler Jun 2021

Data-Driven Artificial Intelligence For Calibration Of Hyperspectral Big Data, Vasit Sagan, Maitiniyazi Maimaitijiang, Sidike Paheding, Sourav Bhadra, Nichole Gosselin, Max Burnette, Jeffrey Demieville, Sean Hartling, David Lebauer, Maria Newcomb, Duke Pauli, Kyle T. Peterson, Nadia Shakoor, Abby Stylianou, Charles S. Zender, Todd C. Mockler

Michigan Tech Publications, Part 1

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform …

Go to article

Clustering And Neighbouring Technique Based Energy-Efficient Routing For Wsns, Ahmed Adil Alkadhmawee, Mohanad Abdulkareem Hasan Hasab, Enas Wahab Abood Jun 2021

Clustering And Neighbouring Technique Based Energy-Efficient Routing For Wsns, Ahmed Adil Alkadhmawee, Mohanad Abdulkareem Hasan Hasab, Enas Wahab Abood

Karbala International Journal of Modern Science

Energy efficiency is the main prerequisite for the permanent and reliable operation of wireless sensor networks (WSNs). Clustering techniques are designed to build energy-efficient networks that enhance network lifetime. Clustering poses certain challenges that directly affect the network performance such as the cluster head selection and routing process. This paper proposes an approach called Clustering and Neighbouring technique Based Energy-Efficient Routing (CNBEER). The CNBEER approach utilises the clustering algorithm and neighbouring technique to prolong the network lifetime by reducing its total energy consumption. The clustering method divides a network into equal-sized clusters to mitigate inessential energy consumption. Moreover, the clustering …

Go to article

Influence Maximization Based On A Non-Dominated Sorting Genetic Algorithm, Elaf Adel Abbas, Huda Naji Nawaf Jun 2021

Influence Maximization Based On A Non-Dominated Sorting Genetic Algorithm, Elaf Adel Abbas, Huda Naji Nawaf

Karbala International Journal of Modern Science

Influence Maximization (IM) is a problem represented by a set of users who are specified in advance and are usually called the seed. The latter can influence their friends, who can in turn influence others and so on until it reaches the largest number of users within the network. This issue is of ultimate importance in a variety of fields. In the current study, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been adopted in influence maximization to produce the so-called NSGAII based IM algorithm (NSGAII-IM). Principally, the population should be represented with individuals of variable lengths as the seed …

Go to article

First Prev Next Last