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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences
Bakhan Tofiq Ahmed
Sulaimani Polytechnic University
Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Data mining techniques for lung and breast cancer diagnosis: A review Bakhan Tofiq Ahmed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i2.pp93-103

Abstract

Today, cancer counted as the riskier disease than the other diseases in the globe. There are many cancer forms like leukemia, skin cancer, and stomach cancer but lung and breast cancer are the most common forms that many people suffered from. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Data-mining classification algorithms had a vital role in predicting and recognizing both benign and malignant cell. Several classifiers are available to classify the usual and unusual cells such as decision-tree, artificial-neural net, SVM, and KNN. This paper presents a systematic review about the most well-known data-mining classification algorithms for lung and breast cancer diagnose. A brief review about KDD and the data-mining concept has demonstrated. The Decision-Tree (D-Tree), ANN, Support-vector-machine, and naïve Bayes classifier that is widely utilized in the biomedical field has been reviewed along with the some algorithms such as C4.5, Cart, and Iterative -Dichotomiser 3 ‘ID3’. A comparison has been done among various reviewed papers in terms of accuracy that used various data-mining classification algorithms to propose the lung and breast cancer diagnosis system. The experimental results of the reviewed papers showed that the Multilayer Perceptron (MLP) and Logistic Regression (LR) gave a higher accuracy of 99.04% and 98.1%, respectively.
A systematic overview of secure image steganography Bakhan Tofiq Ahmed
International Journal of Advances in Applied Sciences Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.408 KB) | DOI: 10.11591/ijaas.v10.i2.pp178-187

Abstract

Information is a vital thing that needs to be secured and well protected during transmission between two or more parties over the internet. This can be achieved by steganography technology. Steganography is the concealing science in which the information is concealed inside other information in a way that the concealed information cannot be detectable by the human eye. Many ways are available to hide data inside a cover media for example text, image, and audio steganography, but image steganography is the most utilized technique among the others. Secure image steganography has a high-security level than traditional technique by combining steganography with cryptography due to encrypting secret information by cryptography algorithm before embedding it into the cover media by steganography algorithm. In this paper, a systematic review has been presented about secure image steganography and its renowned types. Many researchers proposed secure image steganography by using various cryptography and steganography algorithms which have been reviewed. The least significant bit ‘LSB’ was the renowned steganography algorithm which has been used by researchers due to its simplicity, while various cryptography algorithms like advanced encryption standard (AES) and blowfish have been used to propose secure image steganography in the reviewed papers. The comparison among the reviewed papers indicated that the LSB with hash-RSA gave a greater peak signal-noise ratio ‘PSNR’ value than the others which was 74.0189 dB.
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