Yasmin Mohd Yacob
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Preferred name
Yasmin Mohd Yacob
Official Name
Yasmin, Mohd Yacob
Alternative Name
Yacob, Yasmin M.
Yacob, Yasmeen Mohd
Yacob, Yasmin Binti Mohd
Main Affiliation
Scopus Author ID
57188556952
Researcher ID
GIV-6266-2022

Research Output

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Nucleus segmentation in pap smear images using image processing techniques

2024-03-07 , Alslatie M. , Alquran H. , Wan Azani Wan Mustafa , Naser M.M. , Yasmin Mohd Yacob

Cervical cancer is caused by the growth of abnormal cells in the cervix's lining. Human papillomavirus (HPV), a sexually transmitted infection, plays a role in most cervical cancers. Therefore, cervical cancer can be avoided by having regular screenings and being vaccinated against HPV infection. The term pap-smear refers to human cell samples stained using the Papanicolaou method. The Papanicolaou method is used to detect precancerous cell changes before they become invasive cancer. The smear cell image is composed of a nucleus and cytoplasm. Cancer prevalence is determined by the shape and structure of the nucleus. Therefore, the segmentation of the nucleus is an essential step in detecting cancer. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. Our proposed method used a median filter to remove noise and a non-linear contrast stretching to enhance the Pap smear images. Then, we used Bradley thresholding for the segmented cervical nucleus. The main impact of this paper will assist doctors in diagnosing cervical cancer based on Pap smear images and increase the accuracy percentages compared to the conventional method.

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Data Security Implementation using Data Encryption Standard Method for Student Values at the Faculty of Medicine, University of North Sumatra

2021-03-01 , Ikhwan A. , Rafikha Aliana A Raof , Phaklen Ehkan , Yasmin Mohd Yacob , Syaifuddin M.

It is undeniable that advances in technology and the rapid spread of information are also accompanied by an increase of crime in the IT field, very valuable information is sought by criminals in the IT field in order to be misused so that they gain enormous profits. One of the information is student value data, so a system is needed that applies a cryptographic algorithm that can secure the information in this case the method used is securing student value data using the Data Encryption Standard (DES) algorithm. DES is asymmetrical cryptographic algorithm and is also classified as a cipher block with a 64-bit key size. DES converts plaintext into a ciphertext of the same size, 64 bits using a 56-bit internal key. By building a system to implement the DES algorithm in securing data values, it is hoped that it can help the Faculty of Medicine, University of North Sumatra in protecting the confidentiality of data values from irresponsible parties.

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Implementation of image file security using the advanced encryption standard method

2023 , Ali Ikhwan , Rafikha Aliana A Raof , Phak Len Al Eh Kan , Nuri Aslami , Yasmin Mohd Yacob

The application of technology in this era has entered digitalization and is modern. Therefore, we are already in an era of advanced and rapid technological development. It has become a human need to exchange information in every activity. Documents that contain information that is frequently sought or used. The document's use also includes essential information. Document security is undoubtedly a significant factor in prioritizing important information in a document to prevent unauthorized people from misusing the document's vital information. Cryptography is a method of overcoming document security issues so that third parties cannot read the information or messages contained within the document. The 128-bit advanced encryption standard (AES) algorithm is one of the algorithms included in the cryptography technique. Additionally, it can be combined with operation modes such as electronic codebook (ECB) and cipher block chaining (CBC) to create an application that can generate random codes to improve the security of the data contained in the document.

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Wide scope and fast websites phishing detection using URLs lexical features

2017-01-03 , Ammar Yahya Daeef , R Badlishah Ahmad , Yasmin Mohd Yacob , Ng Yen Phing

Phishing is a considerable problem differs from the other security threats such as intrusions and Malware which are based on the technical security holes of the network systems. The weakness point of any network system is its Users. Phishing attacks are targeting these users depending on the trikes of social engineering. Despite there are several ways to carry out these attacks, unfortunately the current phishing detection techniques cover some attack vectors like email and fake websites. Therefore, building a specific limited scope detection system will not provide complete protection from the wide phishing attack vectors. This paper develops detection system with a wide protection scope using URL features only which is relying on the fact that users directly deal with URLs to surf the internet and provides a good approach to detect malicious URLs as proved by previous studies. Additionally, Anti-phishing solutions can be positioned at different levels of attack flow where most researchers are focusing on client side solutions which turn to add more processing overhead at the client side and lead to losing the trust and satisfaction of the users. Nowadays many organizations make centralized protection of spam filtering. This paper proposes a system which can be integrated into such process in order to increase the detection performance in a real time. The simulation results of the proposed system showed a phishing URLs detection accuracy with 93% and provided online process of a single URL in average time of 0.12 second.

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Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

2022-11-01 , Alsalatie M. , Alquran H. , Wan Azani Wan Mustafa , Yasmin Mohd Yacob , Alayed A.A.

The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors’ increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.

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Pap smear images classification based on surrounding tissues: A comparative study

2024-03-07 , Alsalatie M. , Wan Azani Wan Mustafa , Nammneh L. , Almashakbah F.F. , Alquran H. , Yasmin Mohd Yacob

Cervical cancer is one of the most known health problems faced by women around the world. Early detection of cervical cancer may reduce the mortality rate. Pap smear images are new techniques used for screening cervical cancer. This paper excludes the nucleus of pap smear images, and the resultant images are classified into seven classes based on the surrounding region nucleus. Automated features are extracted using three pre-trained convolutional neural networks (CNN). The resultant features are twenty-one. The principal component analysis reduces the dimensionality and selects the most significant features into ten features. These features are fed to two types of machine learning algorithms: support vector machine (SVM) classifier and random forest classifier. The support vector machine classifier achieved the highest accuracy for seven classes, reaching 93.1%. This method will help the physicians in the diagnosis of cervical cancer depending on the tissues, not the nucleus. Furthermore, the result can be enhanced using a huge amount of data.

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Smart pandemic-safe premise system

2023 , Yasmin Mohd Yacob , Azmi Mohd Nasarudin , Priyatharishine Renganathan , Muhamad Zainularifin Zainal , Nurhazwani Mohd Kamal , Harsa Amylia Mat Sakim

Ensuring hygiene, limiting number of people in a closed space and adhere to normal body temperature are concerns to be living in the COVID-19 pandemic era. To date, not only software but also hardware-based works have been developed to address the concerning issue. Nevertheless, most of the works focused in one concerned issue and not many attempted to integrate into the same device. This work integrated three parts which are temperature display, automatic sanitizer dispenser, and entrance and exit with limit counter in the same device for a premise by incorporating the use of temperature, ultrasonic and infrared (IR) sensors on top of Arduino microcontroller board. The developed device buzzes when body temperature exceed 38 degree Celcius, counts people in and out in a premise and allows 20 people to enter the premises at one time and releases hand-sanitizer when hand is detected. The developed work helps to maintain health and preventing disease, especially through cleanliness and limiting number of people to promote physical distancing in a premise. On top of that, automatic sanitizer dispenser may reduce environmental pollution via reduction of sanitizer bottles. This system also reduces dependability to man-power and concurrently reduces the risk of infection to workers.

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