Pub Date : 2023-08-31DOI: 10.31642/jokmc/2018/100227
Suhaam Adnan Abdul kareem, Zena Fouad Rasheed
Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancers effective and early detection. The use of machine learning techniques in biomedicine and bioinformatics to categorize cancer patients into high- or low-risk groups was investigated by numerous research teams. It is necessary that machine learning tools can recognize important features in complex datasets. Here we present a machine learning approach to cancer detection, and to the identification of genes critical for the diagnosis of cancer .We used the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and Gradient Boosting (GB) that provide results that are more accurate than those of current models. Each model's accuracy, including SVM, KNN, RF, and GB, was (97.41%, 89.3%, 88.1%, and 85.7%), respectively. The SVM has the highest precision among machine learning algorithms. By creating a machine learning-based predictive system for early detection, our findings can help to decrease the prevalence of cancer disease.
{"title":"A Machine Learning Model for Cancer Disease Diagnosis using Gene Expression Data","authors":"Suhaam Adnan Abdul kareem, Zena Fouad Rasheed","doi":"10.31642/jokmc/2018/100227","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100227","url":null,"abstract":"Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancers effective and early detection. The use of machine learning techniques in biomedicine and bioinformatics to categorize cancer patients into high- or low-risk groups was investigated by numerous research teams. It is necessary that machine learning tools can recognize important features in complex datasets. Here we present a machine learning approach to cancer detection, and to the identification of genes critical for the diagnosis of cancer .We used the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and Gradient Boosting (GB) that provide results that are more accurate than those of current models. Each model's accuracy, including SVM, KNN, RF, and GB, was (97.41%, 89.3%, 88.1%, and 85.7%), respectively. The SVM has the highest precision among machine learning algorithms. By creating a machine learning-based predictive system for early detection, our findings can help to decrease the prevalence of cancer disease.","PeriodicalId":499493,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136035244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.31642/jokmc/2018/100225
Rasha Ali Dihin, Ebtesam AlShemmary, Waleed Al-Jawher
Diabetic retinopathy (DR) impacts over a third of individuals diagnosed with diabetes and stands as the leading cause of vision loss in working-age adults worldwide. Therefore, the early detection and treatment of DR can play a crucial role in minimizing vision loss. This research paper proposes a novel technique that combines Wavelet and multi-Wavelet transforms with Swin Transformer to automatically identify the progression level of diabetic retinopathy. A notable innovation of this study lies in the implementation of the multi-Wavelet transform for extracting relevant features. By incorporating the resulting images into the Swin Transformer model, a unique approach is introduced during the feature extraction phase. The researchers conducted experiments using the publicly available Kaggle APTOS 2019 dataset, which comprises 3662 images. The achieved training accuracy in the experiments was an impressive 97.78%, with a test accuracy of 97.54%. The highest accuracy observed during training reached 98.09%. In comparison, when applying the multi-Wavelet approach to multiclass classification, the training and validation accuracies were 91.60% and 82.42%, respectively, with a testing accuracy of 82%. These results indicate that the multi-Wavelet approach outperforms alternative methods in the study. The model demonstrated exceptional performance in binary classification tasks, exhibiting high accuracies on both the training and test sets. However, it is important to note that the model's accuracy decreased when employed in multiclass classification, emphasizing the need for further investigation and refinement to handle more diverse classification scenarios.
{"title":"Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet","authors":"Rasha Ali Dihin, Ebtesam AlShemmary, Waleed Al-Jawher","doi":"10.31642/jokmc/2018/100225","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100225","url":null,"abstract":"Diabetic retinopathy (DR) impacts over a third of individuals diagnosed with diabetes and stands as the leading cause of vision loss in working-age adults worldwide. Therefore, the early detection and treatment of DR can play a crucial role in minimizing vision loss. This research paper proposes a novel technique that combines Wavelet and multi-Wavelet transforms with Swin Transformer to automatically identify the progression level of diabetic retinopathy. A notable innovation of this study lies in the implementation of the multi-Wavelet transform for extracting relevant features. By incorporating the resulting images into the Swin Transformer model, a unique approach is introduced during the feature extraction phase. The researchers conducted experiments using the publicly available Kaggle APTOS 2019 dataset, which comprises 3662 images. The achieved training accuracy in the experiments was an impressive 97.78%, with a test accuracy of 97.54%. The highest accuracy observed during training reached 98.09%. In comparison, when applying the multi-Wavelet approach to multiclass classification, the training and validation accuracies were 91.60% and 82.42%, respectively, with a testing accuracy of 82%. These results indicate that the multi-Wavelet approach outperforms alternative methods in the study. The model demonstrated exceptional performance in binary classification tasks, exhibiting high accuracies on both the training and test sets. However, it is important to note that the model's accuracy decreased when employed in multiclass classification, emphasizing the need for further investigation and refinement to handle more diverse classification scenarios.","PeriodicalId":499493,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136035246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.31642/jokmc/2018/100226
Ayaat Waleed, Najlae Falah Hameed Al Saffar
Scalar multiplication is the fundamental operation in the elliptic curve cryptosystem. It involves calculating the integer multiple of a specific elliptic curve point. It involves three levels: field, point, and scalar arithmetic. Scalar multiplication will be significantly more efficient overall if the final level is improved. By reducing the hamming weight or the number of operations in the scalar representation, one can raise the level of scalar arithmetic. This paper reviews some of the algorithms and techniques that improve the elliptic curve scalar multiplication in terms of the third level.
{"title":"Elliptic Curve Scalar Multiplication Operation: a Survey Study","authors":"Ayaat Waleed, Najlae Falah Hameed Al Saffar","doi":"10.31642/jokmc/2018/100226","DOIUrl":"https://doi.org/10.31642/jokmc/2018/100226","url":null,"abstract":"Scalar multiplication is the fundamental operation in the elliptic curve cryptosystem. It involves calculating the integer multiple of a specific elliptic curve point. It involves three levels: field, point, and scalar arithmetic. Scalar multiplication will be significantly more efficient overall if the final level is improved. By reducing the hamming weight or the number of operations in the scalar representation, one can raise the level of scalar arithmetic. This paper reviews some of the algorithms and techniques that improve the elliptic curve scalar multiplication in terms of the third level.","PeriodicalId":499493,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136035248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-13DOI: 10.31642/jokmc/2018/060300
Adil Alrammahi
Two properties must be available in order to construct a fractal set. The first is the selfsimilarity of the elements. The second is the real fraction number dimension. In this paper,condensation principle is introduced to construct fractal sets. Condensation idea is represented in threetypes. The first is deduced from rotation –reflection linear transformation. The second is dealt withgroup action. The third is represented by graph function.
{"title":"Condensation to Fractal Shapes Constructing","authors":"Adil Alrammahi","doi":"10.31642/jokmc/2018/060300","DOIUrl":"https://doi.org/10.31642/jokmc/2018/060300","url":null,"abstract":"Two properties must be available in order to construct a fractal set. The first is the selfsimilarity of the elements. The second is the real fraction number dimension. In this paper,condensation principle is introduced to construct fractal sets. Condensation idea is represented in threetypes. The first is deduced from rotation –reflection linear transformation. The second is dealt withgroup action. The third is represented by graph function.","PeriodicalId":499493,"journal":{"name":"Journal of Kufa for Mathematics and Computer","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135907158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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