Papers by Sulaiman Olaniyi Abdulsalam
Auerbach Publications eBooks, Oct 8, 2023
International Journal of Information Technology and Computer Science, Nov 8, 2017
In this paper, a combination of dimensionality reduction technique, to address the problems of hi... more In this paper, a combination of dimensionality reduction technique, to address the problems of highly correlated data and selection of significant variables out of set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes is proposed. One-Way-ANOVA is employed for feature selection to obtain an optimal number of genes, Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) are employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classification method. Combining feature selection and feature extraction into a generalized model, a robust and efficient dimensional space is obtained. In this approach, redundant and irrelevant features are removed at each step; classification presents an efficient performance of accuracy of about 98% over the state of art.
ICST Transactions on Scalable Information Systems, Sep 25, 2017
Feature extract ion is a proficient method for reducing dimensions in the analysis and prediction... more Feature extract ion is a proficient method for reducing dimensions in the analysis and prediction of cancer classification. Microarray procedure has shown great importance in fetching informat ive genes th at needs enhancement in diagnosis. Microarray data is a challenging task due to high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, a comparative study to demonstrate the effectiveness of feature ext raction as a dimensionality reduction process is proposed, and concludes by investigating the most efficient approach that can be used to enhance classification of microarray. Principal Co mponent Analysis (PCA) as an unsupervised technique and Partial Least Square (PLS) as a supervised technique are considered, Support Vector Machine (SVM) classifier were applied on the dataset. The overall result shows that PLS algorithm provides an improved performance of about 95.2% accu racy compared to PCA algorith ms .
LAUTECH Journal of Engineering and Technology, Jun 21, 2024
Pelican Optimization Algorithm-based Convolutional Neural Network (POA-CNN) method for the automa... more Pelican Optimization Algorithm-based Convolutional Neural Network (POA-CNN) method for the automated identification of pulmonary disorders such as COVID-19 and pneumonia is proposed in this research. The study aims to enhance the efficiency of CNN models in diagnosing lung diseases by using the Pelican Optimization Algorithm (POA), and by addressing drawbacks like a lack of flexibility in hyperparameter modifications. The three primary phases of the model are feature extraction via POA-based hyperparameter optimization, image classification, and image pre-processing. This approach improves existing systems' performance in detecting pulmonary diseases, highlighting the potential of deep learning in identifying and categorizing human diseases. The study uses resizing, grayscale, and augmentation methods to optimize an existing CNN model. A Convolutional Neural Network (CNN) is then applied to classify Pneumonia and COVID-19 cases. The proposed model achieves an accuracy rate of 97.28% and 97.00%, outperforming existing models. This technique is effective in detecting and classifying other pulmonary diseases and can be used to automatically detect and classify these diseases. Higher accuracy findings show how successful the model is, making it a useful tool for pulmonary illness identification.
Artificial Intelligence in Medical Virology
IAES International Journal of Artificial Intelligence (IJ-AI)
Customer predictive analytics has shown great potential for effective churn models. Thriving in t... more Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has been discovered. A support vector machine is employed as the foundational learner, and a churn prediction model is constructed based on each analysis of variance. The separation of churn data revealed by experimental assessment is recommended for churn prediction analysis. Customer attrition is high, but an instantaneous support can ensure that customer needs are addressed and assess an em...
Institution of Engineering and Technology eBooks, Nov 22, 2022
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
International Journal of Advanced Computer Science and Applications
The occurrence of coronavirus (COVID-19), which causes respiratory illnesses, is higher than in 2... more The occurrence of coronavirus (COVID-19), which causes respiratory illnesses, is higher than in 2003. (SARS). COVID-19 and SARS are both spreading over regions and infecting living beings, with more than 73,435 deaths and more than 2000 deaths documented as of August 12, 2020. In contrast, SARS killed 774 lives in 2003, whereas COVID-19 claimed more in the shortest amount of time. However, the fundamental difference between them is that, after 17 years of SARS, a powerful new tool has developed that could be utilized to combat the virus and keep it within reasonable boundaries. One of these tools is machine learning (ML). Recently, machine learning (ML) has caused a paradigm shift in the healthcare industry, and its use in the COVID-19 outbreak could be profitable, especially in forecasting the location of the next outbreak. The use of AI in COVID-19 diagnosis and monitoring can be accelerated, reducing the time and cost of these processes. As a result, this study uses ANN and CNN techniques to detect COVID-19 from chest x-ray pictures, with 95% and 75% accuracy, respectively. Machine learning has greatly enhanced monitoring, diagnosis, monitoring, analysis, forecasting, touch tracking, and medication/vaccine production processes for the Covid-19 disease outbreak, reducing human involvement in nursing treatment.
Communications in Computer and Information Science
Spam is a major problem of electronic mail system that has enjoyed extensive discourse. E-mail ha... more Spam is a major problem of electronic mail system that has enjoyed extensive discourse. E-mail has been greatly abused by spammers to disseminate unwanted messages and spread malicious contents. Several anti-spam systems developed have been greatly abused and this is as evident in the proliferation of Spammer’s activities. Observing this fact, a protective mechanism to countermeasure the ever-growing spam problem is indeed inevitable. In this paper, a heuristic approach is proposed which employs a standard normalized Spammer’s languages harvested from Google and Yahoo spam language data set to build the knowledge base. The spam languages were prioritized based on the frequency of occurrence in the two global data sets. A threshold of 5% was established for a user without spamming history while 3% was set for a suspected spammer. A platform independent system was designed and implemented to monitor users’ mail in real time. As soon as the threshold is reached the user will be alerted...
Recently, data mining has attracted a great deal of attention in the information industry and in ... more Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was ...
The world over, and especially in Africa and Asia, couples show a preference for particular sex o... more The world over, and especially in Africa and Asia, couples show a preference for particular sex of children; eithermale or female. This preference may arise due to economic reasons, customs of the people, or simply for a “genderbalanced family”. Whatever the reasons, the fact still remains that couples would like to be able to choose the sex oftheir children. While there are various options to achieve sex selection, all of them are either too expensive or tooinvasive. This paper presents how Shettles’ method being the least expensive and the most reliable method ofpreconception sex selection was modeled to enable automation. The results show that it is a more consistent andreliable method for gender selection. In addition, the result also shows that the Shettles’ method lends itselffavourably to computer programming and would be very useful in the lives of couples that desire a particular genderof offspring. Keywords : Computer assisted, Preconception, Sex selection, Model, Programm...
Organizations have been collecting data for decades, building massive data warehouses in which to... more Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving ...
Walailak Journal of Science and Technology (WJST), 2021
As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infect... more As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique res...
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2014
The development of network technologies and application has promoted network attack both in numbe... more The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by us...
Computing Information Systems Development Informatics and Allied Research Journal, 2012
Automated Teller Machine (ATM) has gained widespread acceptance as a convenient medium to facilit... more Automated Teller Machine (ATM) has gained widespread acceptance as a convenient medium to facilitate financial transaction without need for human agent. However, ATM deployers are facing challenges in maximizing the uptime of their ATMs as a result of wide gap in fault detection, notification and correction of the ATMs. One way to ameliorate this situation is through intelligent monitoring of ATM by resident software agents that monitor the device real time and report faulty components real time to facilitate quick response. We proposed an architecture for rule-based, intelligent agent based monitoring and management of ATMs. Agents are used to perform remote monitoring on the ATMs and control function such software maintenance. Such agents can detect basic events or correlate existing events that are stored in a database to detect faults. A system administrator can securely modify the monitoring policies and control functions of agents. The framework presented here includes software fault monitor, hardware fault monitor and transaction monitor. A set of utility support agents: caller agent and log agent are used to alert network operator and log error and transaction information in a database respectively. at-1, stuck-at-0 faults in digital circuits validate the point that faulty circuits dissipates more and hence draw more power.
International Journal on Recent and Innovation Trends in Computing and Communication, 2015
Institutions, companies and organisations where security and net productivity is vital, access to... more Institutions, companies and organisations where security and net productivity is vital, access to certain areas must be controlled and monitored through an automated system of attendance. Managing people is a difficult task for most of the organizations and maintaining the attendance record is an important factor in people management. When considering the academic institute, taking the attendance of non-academic staff on daily basis and maintaining the records is a major task. Manually taking attendance and maintaining it for a long time adds to the difficulty of this task as well as wastes a lot of time. For this reason, an efficient system is proposed in this paper to solve the problem of manual attendance. This system takes attendance electronically with the help of a fingerprint recognition system, and all the records are saved for subsequent operations. Staff biometric attendance system employs an automated system to calculate attendance of staff in an organization and do further calculations of monthly attendance summary in order to reduce human errors during calculations. In essence, the proposed system can be employed in curbing the problems of lateness, buddy punching and truancy in any institution, organization or establishment. The proposed system will also improve the productivity of any organization if properly implemented.
International Journal of Computer Applications Technology and Research, 2013
Short Message Service (SMS) is the most powerful tool in terms of communication especially for mo... more Short Message Service (SMS) is the most powerful tool in terms of communication especially for mobile users. It does not limit anyone regardless of high-or low-end mobile phones for as long as they can receive and send messages anytime, anywhere. It was revealed that, lack of adequate communication technology in an organization leads to a number of issues that make such organization to perform less. In order to fully utilize the mobile phones, this study has come up with a fast way where users can get information quickly without spending more. This study aimed to promote access to such information through the use of Short Message Service (SMS) and improve transparency, reliability, usability of the information for organization. The proposed SMS application will provide multi level local authentication to the SMS gateway service. The application was developed based on the Unified Software Development Process or in short, the Unified Process being a components-based system. The programming languages used are PHP and JAVA. The resulting SMS application is found to be usable and economical.
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Papers by Sulaiman Olaniyi Abdulsalam