Papers by Akinrotimi Akinyemi
Studies in big data, 2020
The Blockchain technology deals with, and assures the security of assets and information over its... more The Blockchain technology deals with, and assures the security of assets and information over its network. However, if a breach occurs in some of its security features, and financial crimes such as fraud or tax evasion occurs, the law will have to be evoked to take its full course. Also, the Blockchain technology and the processes that accompany its implementation, exemplify a major deviation from the existing state of affairs in most financial activities, especially in the exchange of a legal tender for goods and services. Blockchain applications present contemporary methods of initiating and embarking on financial transactions in ways that do not conform to current legal structures. As such, having the judiciary decide on punitive measures for blockchain related financial crimes will require a meticulous assessment and revision of the rules guiding legal practice. It may be necessary for most countries to establish a nexus between certain scientific modules and ratification processes, so as to ensure that the modus operandi of blockchain applications agree with the law. This chapter seeks to provide a broad view of the relationship between blockchain and the Law, its effects on legal activities and how the Law can be used as a tool of protection in blockchain related transactions.
Studies in big data, 2020
Sensors
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness iden... more Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal ...
Anale. Seria Informatică., 2019
Surface Electromyography (sEMG) signals have been found to be useful in developing methods of re... more Surface Electromyography (sEMG) signals have been found to be useful in developing methods of recognizing hand gestures using digital signal processing devices or ensemble approaches. In building hand gesture recognition models, it is essential to avoid or reduce computational complications which may result in complex circuit connections while implementing the model. As such, simple but efficient approaches in achieving the desired results are required. A explicable model for hand gesture recognition, using Flexible Neural Trees (FNTs) and sEMG signals is presented in this paper. sEMG is an approach of detecting and documenting the electrical impulses of the muscles, from the surface of the skin. The feed forward Neural Network approach is first used in generating the FNT model before improving it, using predefined simple instruction sets. The FNT model helped to avoid complex computations while building the model and provided a high recognition rate. The experimental outcomes obtained from the developed model shows that the model is capable of classifying six different hand gestures at an accuracy of 98.56%.
Journal of Computer Science and Control Systems, 2021
The heart is a vital organ in the human body. In the process of diagnosing heart diseases, severa... more The heart is a vital organ in the human body. In the process of diagnosing heart diseases, several data are often generated and various data mining procedures are often utilized in sieving out the most useful data for ascertaining the presence or absence of the disease, as a final decision. The set of useful data which is eventually utilised in making this final decision often contains hidden patterns. The excavation and analysis of these hidden patterns is often carried out, using data classification techniques and comparative analysis between different data classification techniques in order to determine the most efficient one for this purpose, constitute an aspect of data mining where there exists unabated research efforts. In this paper, a heart disease diagnosis decision support system has been developed. The system makes use of 13 attributes and four machine learning algorithms (Xgboost, Catboost, LGBM and KNN) in carrying out the data classification process for determining the presence of heart diseases. A performance analysis process carried out amongst these four data classification techniques in detecting heat diseases, revealed that Xgboost has the best performance, as it gives a higher rate of true positives and lower rate of false negatives, as well as a higher level of accuracy, compared to the other three classification techniques considered.
Journal of Computer Science and Control Systems, 2022
A critical performance drawback of most fall detection systems is high false alarms. These false ... more A critical performance drawback of most fall detection systems is high false alarms. These false alarms are due to the imbalanced mix of the "fall" and "non-fall" data contained in the processed datasets on one hand, and the inherent limitation of the processing algorithms, on the other hand. To tackle this false alarm problem, a two-tier solution approach which entails Synthetic Minority Over-Sampling Technique (SMOTE) and hybrid of two machine learning algorithms (Multiple-Kernel Support Vector Machine (MK-SVM) and Multinomial Naïve Bayes (MNB), hereafter known as SMOTE-based MKSVM-MNB is proposed. The results of simulation experiments performed using two open-source datasets namely SisFall Dataset and UMAFall Dataset show that SMOTE-based MKSVM-MNB significantly outperforms MKSVM, MNB and MKSVM-MNB in terms of the number of False Negatives (FN) recorded. Also, MKSVM-MNB significantly outperforms MKSVM and MNB in terms of FN.
It is crucial to avoid intrusion in networks; hence, a developing and intrusion detection system ... more It is crucial to avoid intrusion in networks; hence, a developing and intrusion detection system that used a strong mechanism for detecting intrusions is important. Several studies have been conducted in the domain of intrusion detections. However, some of them suffer from high false alarms, in terms of the use of a raw dataset with redundancy. Objective: This paper, therefore, proposes a multi-level dimensionality reduction framework that is based on meta-heuristic optimization and Principal Component Analysis (PCA). Method: In this research, PCA was applied for feature extraction. Genetic Algorithm and Particle Swarm Optimization, that is GA-PSO, algorithms were utilized for feature selection to extract the most discriminative features to develop intrusion detection model. In the classification phase, both Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms were used to develop intrusion detection, using kddcup.data_10_percent dataset. Result: Experimental results reveal that the proposed framework brought about an accuracy of 99.7% and ROC of 99.9%, while the time required building model is 0.23 seconds. Conclusion: To a very high extent, incidences of high false alarm are allayed through the GA-PSO induced feature selection method.
Sensors, 2023
Many individuals worldwide pass away as a result of inadequate procedures for prompt illness iden... more Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
With the huge progress made in data exchange by electronic systems, the need of information secur... more With the huge progress made in data exchange by electronic systems, the need of information security has become a necessity. Due to growth of multimedia application, security has become an important issue of communication and storage of files. Symmetric key cryptography is a common cryptographic technique, which involves, using the same key at both the transmitter and receiver side. The main advantage of symmetric key encryption is its less computational cost, compared to its counterpart-public key encryption. This research work, evaluated the performance of two symmetric cryptography techniques using the Advanced Encryption Standard (AES) and Data Encryption Standard (DES) for the security of plain text messages. The experimental approach was carried out with MATLAB Programming language. The two algorithms scaled on 64 bit key size for its encryptionand their performance was measured based on the encryption and decryption time alongside with the encryption and decryption memory. Th...
Ilorin Journal of Computer Science and Information Technology, Jun 30, 2021
A report from the International Institute of Tropical Agriculture (IITA) shows a record of over 5... more A report from the International Institute of Tropical Agriculture (IITA) shows a record of over 50 improved cassava varieties that are currently developed to improve the quality and quantity of the industrial outputs derived from cassava. These varieties have been in circulation to both small and large scale farmers since 2013. Over the time, it has been discovered from an IITA survey, that farmers have challenges in deciding the cassava variety that would provide the highest percentage yield of desired industrial outputs (Ethanol, flour, Garri, Starch). The trial and error methods adopted to determine the variety to plant has led to wastage, and inconclusive results. Using various methods to guide their decision can be very cumbersome. There are difficulties in tracing track records, as well as retrieval of certain data set from bunch of records. Loss of record or data set would lead to starting the whole methods all over, which is not an efficient way of solving the problem. Objective: This study designed and implemented a decision support system for cassava farming in Nigeria. Method: This study adopts PHP, JavaScript, and HTML at the front-end of a user friendly application developed to handle large records-keeping processes, with MYSQL database at its backend. Results: The DSS produced 3376kg while manual method produced 3200kg of Starch, the DSS yielded 125kg while manual method produced 0kg of Protein and the DSS generated 1500kg while manual method generated 1450kg of Garri when 5000kg of fresh yield of Cassava variety TMS 4(2)1425 was considered. The DSS indicated highest records of farm outputs over manual method. Conclusions: Digital literacy is not imperative for its operation, thereby proffering solutions to the challenge of informed variety investment decisions in small/large cassava farming activities. Against the wastage from previous methods, experimental output from this developed system indicates higher percentage yield of desired industrial cassava output.
Journal of Computer Science and Control Systems , 2021
The heart is a vital organ in the human body. In the process of diagnosing heart diseases, severa... more The heart is a vital organ in the human body. In the process of diagnosing heart diseases, several data are often generated and various data mining procedures are often utilized in sieving out the most useful data for ascertaining the presence or absence of the disease, as a final decision. The set of useful data which is eventually utilised in making this final decision often contains hidden patterns. The excavation and analysis of these hidden patterns is often carried out, using data classification techniques and comparative analysis between different data classification techniques in order to determine the most efficient one for this purpose, constitute an aspect of data mining where there exists unabated research efforts. In this paper, a heart disease diagnosis decision support system has been developed. The system makes use of 13 attributes and four machine learning algorithms (Xgboost, Catboost, LGBM and KNN) in carrying out the data classification process for determining the presence of heart diseases. A performance analysis process carried out amongst these four data classification techniques in detecting heat diseases, revealed that Xgboost has the best performance, as it gives a higher rate of true positives and lower rate of false negatives, as well as a higher level of accuracy, compared to the other three classification techniques considered.
Ilorin Journal of Computer Science and Information Technology, 2021
A report from the International Institute of Tropical Agriculture (IITA) shows a record of over 5... more A report from the International Institute of Tropical Agriculture (IITA) shows a record of over 50 improved cassava varieties that are currently developed to improve the quality and quantity of the industrial outputs derived from cassava. These varieties have been in circulation to both small and large scale farmers since 2013. Over the time, it has been discovered from an IITA survey, that farmers have challenges in deciding the cassava variety that would provide the highest percentage yield of desired industrial outputs (Ethanol, flour, Garri, Starch). The trial and error methods adopted to determine the variety to plant has led to wastage, and inconclusive results. Using various methods to guide their decision can be very cumbersome. There are difficulties in tracing track records, as well as retrieval of certain data set from bunch of records. Loss of record or data set would lead to starting the whole methods all over, which is not an efficient way of solving the problem. Objective: This study designed and implemented a decision support system for cassava farming in Nigeria. Method: This study adopts PHP, JavaScript, and HTML at the front-end of a user friendly application developed to handle large records-keeping processes, with MYSQL database at its backend. Results: The DSS produced 3376kg while manual method produced 3200kg of Starch, the DSS yielded 125kg while manual method produced 0kg of Protein and the DSS generated 1500kg while manual method generated 1450kg of Garri when 5000kg of fresh yield of Cassava variety TMS 4(2)1425 was considered. The DSS indicated highest records of farm outputs over manual method. Conclusions: Digital literacy is not imperative for its operation, thereby proffering solutions to the challenge of informed variety investment decisions in small/large cassava farming activities. Against the wastage from previous methods, experimental output from this developed system indicates higher percentage yield of desired industrial cassava output.
Ilorin Journal of Computer Science and Information Technology, 2022
It is crucial to avoid intrusion in networks; hence, a developing and intrusion detection system ... more It is crucial to avoid intrusion in networks; hence, a developing and intrusion detection system that used a strong mechanism for detecting intrusions is important. Several studies have been conducted in the domain of intrusion detections. However, some of them suffer from high false alarms, in terms of the use of a raw dataset with redundancy. Objective: This paper, therefore, proposes a multi-level dimensionality reduction framework that is based on meta-heuristic optimization and Principal Component Analysis (PCA). Method: In this research, PCA was applied for feature extraction. Genetic Algorithm and Particle Swarm Optimization, that is GA-PSO, algorithms were utilized for feature selection to extract the most discriminative features to develop intrusion detection model. In the classification phase, both Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms were used to develop intrusion detection, using kddcup.data_10_percent dataset. Result: Experimental results reveal that the proposed framework brought about an accuracy of 99.7% and ROC of 99.9%, while the time required building model is 0.23 seconds. Conclusion: To a very high extent, incidences of high false alarm are allayed through the GA-PSO induced feature selection method.
Understanding customer needs is crucial to gaining and retaining customers in a web store, on onl... more Understanding customer needs is crucial to gaining and retaining customers in a web store, on online e-commerce applications. In order to ensure a versatile system for e-commerce, the pattern generated by customers when they click icons to select some particular products on an e-commerce website, based on their choice, ought to be studied, recorded and built-up into a database. Data mining techniques can then be applied in mining and analyzing information for this database in order to help wholesaler and retailers, improve sales, marketing strategies and product advertisement. In this paper, a conceptualized a framework for determining customers’ product choice and factors involved in choosing online commodities is proposed. The system is tailored for phone products.
Artificial intelligence and expert systems have gone a long away in proffering solutions to decis... more Artificial intelligence and expert systems have gone a long away in proffering solutions to decision making problem in various sectors. One of the major challenges faced today in under developed country is access to quality and fast health facilities, which poses a big threat to the health a condition of patients. Accurate medical diagnosis is one of the major ways to sustain good health and live long. In this paper, a neuro-expert system was developed using advanced neuro-fuzzy inference system, taking into consideration the combination of eight attributes or factors and one output for the prediction and diagnosis of cervical cancer. Cervical cancer dataset obtained from cancer medical experts, was used to build the system. An evaluation performance was so as to carried out to determine the level of predictive and explanatory power of the developed system. The resulting test carried on the systems shows a very good predictive model with an accuracy of 93.54%.
Advances in Multidisciplinary & Scientific Research Journal Publication
One of the most widely used techniques often employed in mining knowledgeable information from me... more One of the most widely used techniques often employed in mining knowledgeable information from medical data bases are Data Mining techniques. While Data mining techniques have proved to be effective in building models illustrating important data classes, especially where class attribute is involved in the construction of the classifier, K-Nearest Neighbor (KNN) is has proved to be an indispensible tool, in pattern recognition. The volume of Medical data bases are huge in nature and getting the right component from them, is essential for data scaling and better data normalization before presenting them for classification. Heart disease is one of the leading causes of death in the world today and based on statistics, the number of adults living with heart failure, increased from about 5.7 million between 2009-2012, to about 6.5 million between 2011-2014 (according to the American Heart Association's 2017 Heart Disease and Stroke Statistics Update).There is therefore need to provide medical practitioners with decision support systems that can help them in the early detection of heart conditions. In this paper, the Probabilistic Principal Component Analysis (PPCA), is used to preprocess and extract components from a clinical dataset, in order to provide a more organized and detailed data to be passed into the KNN classifier, thereby allowing for better classification. Experimental results shows that the combination of the PPCA and KNN is very productive for the prediction of heart diseases as it achieved an accuracy of 98.08%.
Journal of Digital Innovations & Contemporary Research in Science, Engineering & Technology, 2018
With the huge progress made in data exchange by electronic systems, the need of information secur... more With the huge progress made in data exchange by electronic systems, the need of information security has become a necessity. Due to growth of multimedia application, security has become an important issue of communication and storage of files. Symmetric key cryptography is a common cryptographic technique, which involves, using the same key at both the transmitter and receiver side. The main advantage of symmetric key encryption is its less computational cost, compared to its counterpart-public key encryption. This research work, evaluated the performance of two symmetric cryptography techniques using the Advanced Encryption Standard (AES) and Data Encryption Standard (DES) for the security of plain text messages. The experimental approach was carried out with MATLAB Programming language. The two algorithms scaled on 64 bit key size for its encryption and their performance was measured based on the encryption and decryption time alongside with the encryption and decryption memory. The performance evaluation shows that when it comes to the computational time, the Data Encryption Standard performs faster than the Advanced Encryption Standard and also takes more of a Higher CPU Memory in processing than the AES symmetric key cryptography method.
Journal of Computer Science and Control Systems (JCSCS), 2018
Typhoid fever is one of the major life threatening diseases, accounting for the death of millions... more Typhoid fever is one of the major life threatening diseases, accounting for the death of millions of people every year apart from contributing to economic backwardness, mostly in Africa. Prompt and accurate diagnosis is a major key in the medical field, the large number of deaths associated with typhoid fever is as a result of many factors which include: poor diagnosis, self-medication, shortage of medical experts and insufficient health institutions. These prompted for the development of a typhoid diagnosis system that can be used by anyone of average intelligence as this will assist in quick diagnosis of the disease despite shortage of health institutions and medical experts. A fuzzy logic technique was used on the labeled set of typhoid fever conditional variables to generate explainable rules for the diagnosis of typhoid fever. The labeled database was divided into five different levels of severity of typhoid fever and the classification accuracies on both the training set and testing set are 95% and 96% respectively. Implementation was carried out using Matlab as front end and MySQL as database.
Journal of Computer Science and Control Systems (JCSCS), 2017
In this paper, a content-based image retrieval system (CBIR), which computes HSV color space simi... more In this paper, a content-based image retrieval system (CBIR), which computes HSV color space similarity among images is presented. Content-based image retrieval process consists of computing a feature vector that characterizes the nature of the images. The images are stored in a feature database. In CBIR system, the user provides a query image and the system calculates the feature vector, and then compares it with a certain image features of the image database. This document gives a brief description of a system developed for retrieving images similar to a query image from a large set of distinct images. It follows an image processing based approach to extract features present in an image. These features are stored in vectors called feature vectors and compared to the feature vectors of query image and thus, the image database is sorted in decreasing order of similarity. An image is partitioned into sub-blocks of equal size as a first step. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by the 3D histogram. A one-to-many matching method is used to compare the query and target image. Euclidean distance is used in retrieving the similar images. The efficiency of the method is demonstrated with the graphical results.
Annals. Computer Science Series Journal "Tibiscus" University of Timişoara, 2019
ABSTRACT: Surface Electromyography (sEMG) signals have been found to be useful in developing meth... more ABSTRACT: Surface Electromyography (sEMG) signals have been found to be useful in developing methods of recognizing hand gestures using digital signal processing devices or ensemble approaches. In building hand gesture recognition models, it is essential to avoid or reduce computational complications which may result in complex circuit connections while implementing the model. As such, simple but efficient approaches in achieving the desired results are required. A explicable model for hand gesture recognition, using Flexible Neural Trees (FNTs) and sEMG signals is presented in this paper. sEMG is an approach of detecting and documenting the electrical impulses of the muscles, from the surface of the skin. The feed forward Neural Network approach is first used in generating the FNT model before improving it, using predefined simple instruction sets. The FNT model helped to avoid complex computations while building the model and provided a high recognition rate. The experimental outcomes obtained from the developed model shows that the model is capable of classifying six different hand gestures at an accuracy of 98.56%.
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Papers by Akinrotimi Akinyemi