|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning显示文摘One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives. | Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 2 | Content Based Automated File Organization Using Machine Learning Approaches显示文摘In the world of big data,it’s quite a task to organize different files based on their similarities.Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user.Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities.Then,the browsing process can be performed quickly and easily.This research aims to design a system to automatically organize files based on their similarities in terms of content.The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system.The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files.The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content.The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows. | Syed Ali Raza Sagheer Abbas Taher M.Ghazal Muhammad Adnan Khan Munir Ahmad Hussam Al Hamadi | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 3 | Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System显示文摘The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%. | Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 4 | IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine显示文摘The Internet of Medical Things(IoMT)enables digital devices to gather,infer,and broadcast health data via the cloud platform.The phenomenal growth of the IoMT is fueled by many factors,including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology.There is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising rapidly.The IoMT is a novel reality transforming our daily lives.It can renovate modern healthcare by delivering a more personalized,protective,and collaborative approach to care.However,the current healthcare system for outdoor senior citizens faces new challenges.Traditional healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor environment.Hence,in this research work,a IoMT based Smart Healthcare of Elderly people using Deep Extreme Learning Machine(SH-EDELM)is proposed to monitor the senior citizens’healthcare.The performance of the proposed SH-EDELM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate,respectively. | Muath Jarrah Hussam Al Hamadi Ahmed Abu-Khadrah Taher M.Ghazal | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 5 | Smart Energy Management System Using Machine Learning显示文摘Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate. | Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员