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| 1 | Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification显示文摘Mental health signifies the emotional,social,and psychological well-being of a person.It also affects the way of thinking,feeling,and situation handling of a person.Stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life.Mental health problems are rising globally and constituting a burden on healthcare systems.Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated.The machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric nosology.This research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and Schizophrenia.These challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the models.Furthermore,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical experts.The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique.This proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%respectively.This framework seems suitable in the case of huge and more diverse multiclass datasets.Furthermore,three vector spaces based on TF-IDF(unigram,bi-gram,and tri-gram)are also tested on the machine learning models and the proposed model. | Maqsood Ahmad Noorhaniza Wahid Rahayu A Hamid Saima Sadiq Arif Mehmood Gyu Sang Choi | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 2 | Hyperchaos and MD5 Based Efficient Color Image Cipher显示文摘While designing and developing encryption algorithms for text and images,the main focus has remained on security.This has led to insufficient attention on the improvement of encryption efficiency,enhancement of hyperchaotic sequence randomness,and dynamic DNA-based S-box.In this regard,a new symmetric block cipher scheme has been proposed.It uses dynamic DNA-based S-box connected with MD5 and a hyperchaotic system to produce confusion and diffusion for encrypting color images.Our proposed scheme supports various size color images.It generates three DNA based Sboxes for substitution namely DNA_1_s-box,DNA_2_s-box and DNA_3_sbox,each of size 16×16.Next,the 4D hyperchaotic system followed by MD5 is employed in a novel way to enhance security.The three DNAbased S-boxes are generated from real DNA sequences taken from National Center for Biotechnology Information(NCBI)databases and are dependent on the mean intensity value of an input image,thus effectively introducing content-based confusion.Finally,Conservative Site-Specific Recombination(CSSR)is applied on the output DNA received from DNA based S-boxes.The experimental results indicate that the proposed encryption scheme is more secure,robust,and computationally efficient than some of the recently published similar works.Being computational efficient,our proposed scheme is feasible on many emergent resource-constrained platforms. | Muhammad Samiullah Waqar Aslam Saima Sadiq Arif Mehmood Gyu Sang Choi | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 3 | Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals显示文摘Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment. | Muhammad Tayyeb Muhammad Umer Khaled Alnowaiser Saima Sadiq Ala’Abdulmajid Eshmawi Rizwan Majeed Abdullah Mohamed Houbing Song Imran Ashraf | 2023 | Computer Modeling in Engineering & Sciences2023，，11： | 0 |
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