Browsing by Author "Adewole, K. S."
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Item Borrowing patterns monitoring in Library: Application of Apriori algorithm(International Journal of Information Processing and Communication, 2015) Adewole, K. S.; Akintola, A. G.; Mabayoje, M. A.; Ogbomon, G. A.Data is a valuable tool for any institution, and with the world advancing in technology, data stored in database management systems are growing in different capacities in almost all organization. The opportunities of database management systems have been explored. However, many organizations have not been able to leverage these opportunities in gaining business intelligence from their repositories. This paper addresses the issue of knowledge discovery from large databases using association rule mining. Apriori algorithm is implemented to discover hidden knowledge from a library database. Data depicting nine (9) different books were used within forty-seven (47) unique transactions. Eighteen (18) unique transactions were generated from the database showing the borrowing pattern of library users. The frequencies of borrowing of books were obtained as well as the associations. The result shows that borrowing a particular book may leads to borrowing another book as revealed in the association between Data structure in C (DS) textbook and Programming in C (C) textbook. The discovered pattern can help librarians in restructuring their bookshelf arrangement, and for book recommendation system. This system can also help students to have good knowledge different related books.Item Building a Spammer Monitoring System Using Heuristic Rule-Based Approach(International Journal of Engineering and Technology, Centre of Professional Research Publications, 2012) Adewole, K. S.; Babatunde, R. S.; Isiaka, R. M.; Abdulsalam, S. O.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 and the suspected mail will be cancelled. The developed model was evaluated for accuracy and effectiveness using three composed email messages. It is recommended among others that this spam preventive model be incorporated in the architecture of every Internet Service Provider.Item A comprehensive survey on low-cost ECG acquisitionsystems:Advancesondesign specifications, challenges and future direction(Elsevier, 2021) Faruk, N.; Abdulkarim, A.; Emmanuel, I.; Folawiyo, Y.; Adewole, K. S.; Mojeed, H. A.; Oloyede, A. A.; Olawoyin, L. A.; Sikiru, I. A.; Nehemiah, M.; Gital, A. Y.; Chiroma, H.; Ogunmodede, J. A.; Almatairi, M.; Katibi, I. A.Avalability of low-cost,reliable,andportableElectrocardiography(ECG)devicesisstillvery onanannualbasisglobally.ThisismoreprevalentinLowandMiddleIncomeCountries CVDsareconfoundedbylatediagnosis,frequently,causedbylackofaccesstoornon availabilityofbasicdiagnosticmodalitiessuchastheECG.Henceeffectivemitigationof reliability,accuracyandenergyefficiency.Thispapertherefore,wasdevelopedtounder theeffectofCVDsinLMICsdependonthedevelopmentofsuchdevicesatlow-costwith (LMICs)wheretherearehugefinancialinstabilityandlackofcriticalinfrastructureand CardiovascularDiseases(CVDs)remainaserioushealthburdenclaimingmillionsoflives standthestateoftheartoflowcostECGacquisitionsystemswithrespecttodesignfea turesandsystemcapabilitiesfordifferentusecases.Inaddition,differentdesignoptions andtaxonomiesofavailablelowcostECGdevices,casestudiesreportsofefficacytests importantinthemedicalworldtoday.Despitethetremendoustechnologicaladvancement, supportservicesforthehealthcaresystem.Effortsaimedatreducingtheprevalenceof have been provided. The paper proposes a generalised ECG framework and provides implementation challenges and open research directionsthatshouldbeconsideredwhendevel opingsuchdevicesforpropermanagementofCVDs.Item Design and Implementation of Information Retrieval System for Laboratory Equipments(Journal of Mathematics Association of Nigeria, ABACUS, 2016) Oladipo, I. D.; Adewole, K. S.; Babatunde, A. O.; Abati, A. I.; Tomori, A. R.; Awotunde, J. B.Getting information relating to user's search criteria on equipment within a University laboratory has posed many challenges since there is no specific system to serve this purpose. It is difficult to handle the whole system manually. The aim of this paper is therefore to design and implement a web-based information retrieval system for University Laboratory equipment. This system offers a powerful internet-based search engine for locating and identifying equipment within a University laboratory while eliminating all unrelated content that a general-purpose search engine would retrieve. The method used involved collecting information needed from the technologist on various equipment in the laboratory and inserted into the retrieval system using SQL server to make query on the database to manage the data and a PHP programming language was used as the server-side scripting language for establishing an efficient querying of information from the database. Java scripting was also used as client-side scripting language for the purpose of adding more interactions to the proposed system. The developed system having been evaluated and assessed thoroughly was found to be efficient, easy to use for locating laboratory equipment in a University.Item Development of an Intrusion Detection System in a Computer Network(International Journal of Computers & Technology (IJCT), 2014) Babatunde, R. S.; Adewole, K. S.; Abdulsalam, S. O.; Isiaka, R. M.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 users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using MicrosoftVisual Basic.Item Efficient Data Hiding System Using Cryptography and Steganography(International Journal of Applied Information Systems, 2012) Abikoye, O. C.; Adewole, K. S.; Oladipupo, A. J.Increase in the number of attack recorded during electronic exchange of information between the source and intended destination has indeed called for a more robust method for securing data transfer. Cryptography and steganography are well known and widely used techniques that manipulate information in order to cipher or hide their existence. These two techniques share the common goals and services of protecting the confidentiality, integrity and availability of information from unauthorized access. In this paper, a data hiding system that is based on audio steganography and cryptography is proposed to secure data transfer between the source and destination. Audio medium is used for the steganography and a LSB (Least Significant Bit) algorithm is employed to encode the message inside the audio file. The proposed system was evaluated for effectiveness and the result shows that, the encryption and decryption methods used for developing the system make the security of the proposed system more efficient in securing data from unauthorized access. The system is therefore, recommended to be used by the Internet users for establishing a more secure communication.Item Feature selection and computational optimization in high-dimensional microarray cancer datasets via InfoGain-modified bat algorithm(Multimedia Tools and Applications, 2022) Hambali, M. A.; Oladele, Tinuke Omolewa; Adewole, K. S.; Sangaiah, A. K.; Gao, W.Achieving a satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task, most importantly in heterogeneous multimedia data. One of the major drawbacks in cancer study is recognizing informative genes from thousands of available genes in microarray data. Traditional feature selection algorithms have failed to scale on large space data like microarray data. Therefore, an effective feature selection algorithm is required to explore the most significant subset of genes by removing non-predictive genes from the dataset without compromising the accuracy of the classification algorithm. The study proposed an information Gain – Modified Bat Algorithm (InfoGain-MBA) features selection model for selecting relevant and informative features from high dimensional Microarray cancer datasets and evaluate the approach with four classifiers - C4.5, Decision Tree, Random Forest and classification and regression tree (CART). The results obtained show that the proposed approach is promising for the classification of microarray cancer data. The random forest has 100% accuracy with few genes in all seven datasets used. Further investigations were also conducted to determine the optimal threshold for each of the datasets.Item Fingerprint Biometric Authentication for Enhancing Staff Attendance System(International Journal of Applied Information Systems, 2013) Oloyede, M. O.; Adedoyin, A. O.; Adewole, K. S.Biometric technology that involves the identification and verification of individuals by analyzing the human body characteristics has been widely used in various aspect of life for different purposes, most importantly as regards this study the issue of staff attendance. Despite the numerous advantages of the biometric system and its impact to various work sectors across the globe, most biometric technology users face the issue of defining the right and accurate biometric technology system that will be cost effective in solving particular problems in specific environment. In this paper, a study was conducted using a telecommunication company in the South West region of Nigeria, in order to determine the specific biometric identifier that can be used to enhance their traditional staff attendance system which presently affects the productivity of the organization. The study was conducted using a quantitative approach by designing a questionnaire as the data collection instrument based on different biometric technologies. The survey involved 37 employees based on stratified random sampling technique. The results however show that fingerprint biometric identifier was found suitable for the staff attendance management system of the organization. It therefore, implies that attention should be paid to several factors before recommending biometric technology as a means of improving the productivity of an organization business processes.Item Fingerprint Biometric-Based Cryptographic System as a Security Approach in Grid Environment(Journal of Computations & Modelling (JCoMod), 2014) Abdulraheem, M.; Aremu, D. R.; Adewole, K. S.; Muhammed, K. J.Interconnection of computer systems for the purpose of sharing resources in grid is increasing on the daily bases. Resource shared in grid is not limited to files alone but also includes computer resources such as memory, processors, etc. The security challenges resulting from this sharing is enormous including authentication, authorization, integrity, availability. These call for research attentions as evidenced from the reviewed literatures. Several researches have proposed cryptographic approaches as a promising solution to the various security challenges. However, issues surrounding the knowledge-based authentication approach motivate the researchers to be more innovative by proposing biometric-based cryptographic model to secure grid resources. This paper examines and analyses existing efforts to tackle these challenges. It also examines Grid Architecture where various components of a grid play major role in resource sharing and securing of grid. Biometric-based model was proposed that provides security for grid users using fingerprint for authentication and authorization in grid due to its ease of collectability.Item Forged Signature Detection Using Artificial Neural Network(African Journal of Computing & ICTs (AJOCICT), 2014) Oladele, Tinuke Omolewa; Adewole, K. S.; Abiodun, T. N.; Oyelami, A. O.Item Forged Signature Detection using Artificial Neural Network(African Journal of Computing & ICT, 2014) Oladele, T. O.; Adewole, K. S.; Oyelami, A. O.; Abiodun, T. N.Crimes and corruptions are practices that gradually cripple the economy of a nation most especially in Nigeria. Nigerian government has strived hard to reduce these acts perpetrated by the citizens. This is evident in the struggles of Economic and Financial Crime Commission (EFCC) and Independence Corrupt Practices and other Related Offences Commission (ICPC) to reduce frauds in both public and private sectors due to signature forgery which attempts to commit financial crimes and other related offences. Forged signature is an illegal copy of signature that looks like a genuine signature usually used for financial fraud. Identity verification (authentication) in computer systems has been traditionally based on something that one has such as key, magnetic or chip card or that one knows such as PIN or password. Things like keys or cards, however, tend to get stolen or lost and passwords are often forgotten or disclosed. In this paper, a neural network algorithm was employed to develop a system that can verify and detect forged signatures. The effect of the signature verification and detection is discussed and its impact on the economy is highlighted. Result of the proposed Java application shows its capability in detecting forged signatures. The system has the capability to learn from previous data and to assist EFCC and ICPC in detecting and investigating fraudulent activities.Item Frequent Pattern and Association Rule Mining from Inventory Database using Apriori Algorithm(African Journal of Computing & ICT, 2014) Adewole, K. S.; Akintola, A. G.; Abdulsalam, S. O.; Ajiboye, A. R.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 found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules.Item Hybridization Algorithms for Cancer Disease Diagnosis Using Microarray Data(U6 Initiative for Development, 2017) Muhammed, K. J.; Oladele, T. O; Adewole, K. S.Cancer is one of the most common deadly diseases in the world. The conventional diagnostic techniques are not always effective as they rely on the physical and morphological appearance of the tumor. The ability of the physicians to effectively treat and cure cancer is directly dependent on their capability to detect cancers at their earliest stages. Early stage prediction and diagnosis is difficult with those conventional techniques such as physical appearance of tumor. However, these techniques are costly, time consuming, requires large laboratory setup and highly skilled persons. There is need for faster, easier, more accurate and effective method, using modern technology to address the challenges. In this dissertation, Hybridized model of Genetic Algorithm and Neural Network was developed and simulated in Weka environment using microarray cancer dataset. Microarray studies are characterized by a low sample number and a large feature space with many features irrelevant to the problem being studied. This makes feature selection a necessary pre-processing step for many analyses, particularly classification. Various stages involved in Genetic Algorithm and Neural Network were studied and simulated. An hybrid model that combines the optimization power of Genetic algorithm for reduction of high dimensional microarray data and Neural network for classification between malignant pleural mesothelioma (MPM) and adenocarcinoma (ADCA) of the lung was proposed. The solution found by the combined Genetic Algorithm and Neural Network performed effectively well. The genetic algorithm reduced 12,533 attributes in the microarray dataset to 748 attributes. The reduced microarray dataset was used to train the multilayer perceptron neural network classifier. The trained classifier achieved 97.5% accuracy when evaluated with the testing microarray dataset. The results presented in this dissertation revealed that the proposed hybrid Genetic Algorithm and Neural Network performs better with over 97% accuracy when used to classify microarray dataset of lung cancerItem IMPLEMENTING MOBILE-LEARNING IN NIGERIA TERTIARY EDUCATIONAL SYSTEM – A Feasibility Study(International Journal of Science and Advanced Technology, 2011) Isiaka, R. M.; Adewole, K. S.; Olayemi, R. T.This paper reports the pilot study on the feasibility of mobile-learning (m-learning) in Nigeria Tertiary Educational system. It investigates the level of availability and usage of mobile devices among students in Nigerian Tertiary Institutions. University of Ilorin- a Federal University and Kwara State University were the case study. One hundred students (100); fifty (50) from each of the universities were randomly sampled for the study. Structured questionnaire was used to elicit the kind of mobile devices being use by the students and the use to which they are currently putting them. T-test statistical inference was used to analyze the mobile and computer usage patterns among the students, the calculated mean for the mobile devices usage pattern (9.43) is greater than that of computer usage pattern (5.30). This shows that students use mobile devices to perform more functions than computer systems. This result was further analyzed using paired samples correlations which show that there is a very weak correlation (0.241) between mobile devices and computer usage patterns. Also, the t-calculated is 18.888 and using degree of freedom of 99 and confidence interval of 0.050 in t-distribution table, the table value is 1.980. It was therefore inferred that since the table value is less than the t-calculated value, there is a significant difference between the mobile devices and computer usage patterns. The various types of mobile devices, and operations or usage to which they are being put to by students were summarized. Furthermore, the academic relevance of these devices was discussed in relation to relevant theories of learning such as; behaviourism, constructivism, and socialism [11] that is enhanced by the usage pattern of mobile devices. It was concluded that the overall intention of blended learning, distance learning or e-learning is becoming more feasible in Africa especially in Nigeria via an emerging concept of m-learning. The green signal is the product of the general advancements in mobile communication technology, the availability, affordability and popularity of mobile devices among the digital native and digital immigrants [9] in Nigerian higher institutions of learning.Item Iris Feature Extraction for Personal Identification using Fast Wavelet Transform (FWT)(International Journal of Applied Information Systems, 2014) Abikoye, O. C.; Sadiku, J. S.; Adewole, K. S.; Jimoh, R. G.Iris is the annular region of the eye bounded by the pupil and the sclera(white of the eye) on either side. The iris has many interlacing features such as stripes, freckles, coronas, radial furrow, crypts, zigzag collarette, rings etc collectively referred to as texture of the iris. This texture is well known to provide a signature that is unique to each subject. All these features are extracted using different algorithms i.e features extraction is the process of extracting information from the iris image. Iris feature extraction is the crucial stage of the whole iris recognition process for personal identification. This is a key process where the two dimensional image is converted to a set of mathematical parameters. The significant features of the iris must be encoded so that comparisons between templates can be made. In this study the feature of the iris is extracted using Fast Wavelet Transform (FWT). The algorithm is fast and has a low complexity rate. The system encodes the features to generate its iris feature codes.Item Malicious accounts: Dark of the social networks(Journal of Network and Computer Applications, 2017) Adewole, K. S.; Anuar, N. B.; Kamsin, A.; Varathan, K. D.; Razak, S. A.Over the last few years, online social networks (OSNs), such as Facebook, Twitter and Tuenti, have experienced exponential growth in both profile registrations and social interactions. These networks allow people to share different information ranging from news, photos, videos, feelings, personal information or research activities. The rapid growth of OSNs has triggered a dramatic rise in malicious activities including spamming, fake accounts creation, phishing, and malware distribution. However, developing an efficient detection system that can identify malicious accounts, as well as their suspicious behaviors on the social networks, has been quite challenging. Researchers have proposed a number of features and methods to detect malicious accounts. This paper presents a comprehensive review of related studies that deal with detection of malicious accounts on social networking sites. The review focuses on four main categories, which include detection of spam accounts, fake accounts, compromised accounts, and phishing. To group the studies, the taxonomy of the different features and methods used in the literature to identify malicious accounts and their behaviors are proposed. The review considered only social networking sites and excluded studies such as email spam detection. The significance of proposed features and methods, as well as their limitations, are analyzed. Key issues and challenges that require substantial research efforts are discussed. In conclusion, the paper identifies the important future research areas with the aim of advancing the development of scalable malicious accounts detection system in OSNs.Item Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier(Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics: Theories and Applications, 2021) Adewole, K. S.; Raheem, M. O.; Abikoye, O. C.; Ajiboye, A. R.; Oladele, Tinuke Omolewa; Jimoh, M. K.; Aremu, D. R.Within the multitude of security challenges facing the online community, malicious websites play a critical role in today’s cybersecurity threats. Malicious URLs can be delivered to users via emails, textmessages, pop-ups or advertisements. To recognize these malicious websites, blacklisting services have been created by the web security community. This method has been proven to be inefficient. This chapter proposed meta-heuristic optimization method for malicious URLs detection based on genetic algorithm (GA) and wolf optimization algorithm (WOA). Support vector machine (SVM) as well as random forest (RF) were used for classification of phishingweb pages. Experimental results showthatWOAreduced model complexity with comparable classification results without feature subset selection. RF classifier outperforms SVM based on the evaluation conducted. RF model without feature selection produced accuracy and ROC of 0.972 and 0.993, respectively, while RF model that is based onWOA optimization algorithm produced accuracy of 0.944 and ROC of 0.987. Hence, in view of the experiments conducted using two well-known phishing datasets, this research shows that WOA can produce promising results for phishing URLs detection task.Item Microarray cancer feature selection: Review, challenges and research directions(International Journal of Cognitive Computing in Engineering (IJCCE), Elsevier B. V., 2020) Hambali, M. A.; Oladele, Tinuke Omolewa; Adewole, K. S.Microarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. Microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed.Item Multi-objective scheduling of MapReduce jobs in big data processing(Multimedia Tools and Applications, 2017) Hashem, I .A. T.; Anuar, N. B.; Marjani, M.; Gani, A.; Sangaiah, A. K.; Adewole, K. S.Data generation has increased drastically over the past few years due to the rapid development of Internet-based technologies. This period has been called the big data era. Big data offer an emerging paradigm shift in data exploration and utilization. The MapReduce computational paradigm is a well-known framework and is considered the main enabler for the distributed and scalable processing of a large amount of data. However, despite recent efforts toward improving the performance of MapReduce, scheduling MapReduce jobs across multiple nodes has been considered a multi-objective optimization problem. This problem can become increasingly complex when virtualized clusters in cloud computing are used to execute a large number of tasks. This study aims to optimize MapReduce job scheduling based on the completion time and cost of cloud service models. First, the problem is formulated as a multi-objective model. The model consists of two objective functions, namely, (i) completion time and (ii) cost minimization. Second, a scheduling algorithm using earliest finish time scheduling that considers resource allocation and job scheduling in the cloud is proposed. Lastly, experimental results show that the proposed scheduler exhibits better performance than other well-known schedulers, such as FIFO and Fair.Item A Network-based Key Exchange Cryptosystem Using Elgamal Algorithm(African Journal of Computing & ICT, 2014) Babatunde, A. O.; Adewole, K. S.; Abdulraheem, M.; Oniyide, S. A.A cryptosystem describes the system where two or more individuals communicate in a secret manner over a public channel. The contents of information shared on a public network need to be protected against unauthorized access using a more secured technique of encryption and decryption. A cipher is the heart of a cryptosystem, which specifies the policy for encryption and decryption. The encryption policy takes the plaintext and a predetermined key, and produces a ciphertext which hides the true meaning of the original plaintext. The decryption policy takes another key, which may be different from the encryption key, and recovers the plaintext from the ciphertext. In this paper, a cryptosystem is developed using Elgamal algorithm for key exchange purpose during information sharing on a public network. The proposed system provides a secure platform for encrypting and decrypting user's key. This key could be used by a symmetric algorithm for file encryption and decryption. The system is found capable of securing user's key from illegal access by an unauthorized person.