Samuel Sunday Udoh - Academia.edu (original) (raw)

Papers by Samuel Sunday Udoh

Communications in computer and information science, 2024

International Journal of Tomography and Simulation, 2013

Fuzzy logic is a known Soft Computing technique for managing imprecise, vague and ambiguous data ... more Fuzzy logic is a known Soft Computing technique for managing imprecise, vague and ambiguous data and uncertain information. Artificial Neural Network is known to generalise from the data it has not encountered before. These attributes of data were found prevalent in information gathered from hepatitis patients. The information given from hepatitis patients are so imprecise as the patient could not explain exactly how they feel, neither were the doctors able to decipher exactly the results of their examination of the patients. These led to the applications of the two techniques in evaluating the diagnosis of hepatitis of 30 patients. The results obtained indicate a strong correlation necessitating the use of a standard scale function to standardise and average the results. The output of this is deemed as the final diagnostic results of each of the 30 patients’ data used for the study. The degree to which a patient suffers from hepatitis reported in the standardised diagnostic results...

Studies in engineering and technology, Apr 6, 2017

In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonl... more In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonlinear problems. Monitoring of variations in Petroleum Products Pipeline (PPP) attributes (flow rate, pressure, temperature, viscosity, density, inlet and outlet volume) which changes with time is complex due to existence of non linear interaction amongst the attributes. The existing works on PPP monitoring are limited by lack of capabilities for pattern recognition and learning from previous data. In this paper, NN models with pattern recognition and learning capabilities are compared with a view of selecting the best model for monitoring PPP. Data was collected from Pipelines and Products Marketing Company (PPMC), Port Harcourt, Nigeria. The data was used for NN training, validation and testing with different NN models such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Feed Forward (GFF), Support Vector Machine (SVM), Time Delay Network (TDN) and Recurrent Neural Network (RNN). Neuro Solutions 6.0 was used as the front-end-engine for NN training, validation and testing while My Structured Query Language (MySQL) database served as the back-end-engine. Performance of NN models was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient (r), Akaike Information Criteria (AIC) and Minimum Descriptive Length (MDL). MLP with one hidden layer and three processing elements performed better than other NN models in terms of MSE, MAE, AIC, MDL and r values between the computed and the desired output.

International Journal of Science and Research Archive

Terrorism and its brutal tendencies constitute a major setback to the development process of the ... more Terrorism and its brutal tendencies constitute a major setback to the development process of the Nigerian economy leading to severe loss of lives, destruction of properties, and a decline of interest in investment by both local and foreign investors. Many models for assessment of terrorist’s activities lack the ability of learning from previous patterns in order to guide pre-emptive actions against future occurrences, and there are no established regional pattern of weaponry, types of attack, as well as types of victims of terrorists’ operations. This study seeks to build a robust intelligent model for recognizing several terrorists’ patterns in each of the six geo-political zones of Nigeria. A data set of 5,503 instances of terrorists’ activities in Nigeria was obtained and a pattern recognition model was built using Artificial Neural Networks (ANN) with 70%, 15%, and 15% data splits for training, validation, and testing respectively. A 10-10-6 ANN architecture was designed and tra...

Tropical Medicine and Infectious Disease

The report of the World Health Organization (WHO) about the poor accessibility of people living i... more The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 ...

Studies in Computational Intelligence, 2009

The experiments aimed to compare machine learning algorithms to create models for the valuation o... more The experiments aimed to compare machine learning algorithms to create models for the valuation of residential premises were conducted using the SAS Enterprise Miner 5.3. Eight different algorithms were used including artificial neural networks, statistical regression and decision trees. All models were applied to actual data sets derived from the cadastral system and the registry of real estate transactions. A dozen of predictive accuracy measures were employed. The results proved the usefulness of majority of algorithms to build the real estate valuation models.

Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accur... more Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accuracy in performance in a number of applications in the last decade. This is due to its ability to cope with uncertainty and precisions adequately when compared with its type-1 counterpart. In this paper, an Interval Type-2 Fuzzy Logic System (IT2FLS) is employed for remote vital signs monitoring and predicting of shock level in cardiac patients. Also, the conventional, Type-1 Fuzzy Logic System (T1FLS) is applied to the prediction problems for comparison purpose. The cardiac patients’ health datasets were used to perform empirical comparison on the developed system. The result of study indicated that IT2FLS could coped with more information and handled more uncertainties in health data than T1FLS. The statistical evaluation using performance metrices indicated a minimal error with IT2FLS compared to its counterpart, T1FLS. It was generally observed that the shock level prediction experime...

International Journal on Advanced Science, Engineering and Information Technology

Artificial Intelligence Research, 2017

The importance of timely detection, classification and response to anomalies on petroleum product... more The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities. ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlatio...

International Journal of Quantitative Research in Education, 2021

International Journal of Business Information Systems, 2020

Proceedings of the 4th International Conference on Medical and Health Informatics, 2020

Incorporating evidence-based healthcare practice would improve patients' response and safety ... more Incorporating evidence-based healthcare practice would improve patients' response and safety and make patients partners in current healthcare practice. This partnership is certain to offer patients the opportunity to guide safety initiatives through data access by clinicians and encourage evidence-based healthcare while alleviating potential medical errors. In this paper, we promote a collaborative model that integrates interrelated concepts for responsive healthcare services that target patient-centred healthcare--with healthcare providers and relevant stakeholders in the loop. The implementation strategies for fulfilling the desired healthcare outcomes as well as design implications are also provided. The model is expected to offer transformative impact that would drive our weak healthcare system for improved healthcare and complement the huge dearth in healthcare services. The outcome is shared prosperity and health, and a mainstream of the people into healthcare decision mak...

The use of Artifical Neural Network (ANN) in modifying and improving the output of existing scien... more The use of Artifical Neural Network (ANN) in modifying and improving the output of existing scientific and economic models has been pragmatic in recent time. In this study, we modify the traditional s-S stochastic inventory model by adding the ANN predicted customers demand at lead time to the re-order quantity. The ANN was designed and implemented using Visual Basic 6.0 programming tools and Microsoft Access as the data- base. Data collected from petrol mega station were standardized to fit the (0,1) ANN sigmoid transfer function domain. Backpropagation algorithm was used in training the Network. At every re-order point the expected period (days) of arrival of goods was inputted into the system and the quantity of goods to be demanded (Dp ) by the customers before the arrival of new stock was predicted and added to the s-S re-order quantity. The correlation coefficient of 0.97 and 0.58 were obtained for the modified s-S and traditional s-S inventory models respectively. The modifie...

The need for adequate attention to security of lives and properties cannot be over-emphasised. Ex... more The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented.

Soft Computing, 2020

The task of optimizing service quality in wireless networks is a continuous research that require... more The task of optimizing service quality in wireless networks is a continuous research that requires the design of efficient channel allocation schemes. The problem is how limited channel resources can be maximally utilized, to guarantee seamless communication while maintaining excellent service quality. Whereas, fixed channel allocation (FCA) schemes treat new and handoff calls equally without preference to normally prioritized handoff calls; dynamic channel allocation (DCA) schemes accommodate users mobility in randomly changing network conditions. However, classical Erlang-B models are deficient and do not consider users mobility and dynamically changing traffic of the mobile network environment. A modified Erlang-B dynamic channel allocation (MEB-DCA) scheme is therefore introduced in this paper, for improved network performance. The MEB-DCA algorithm introduces a conditional threshold for handoff request assignment to ensure that communication systems do not unnecessarily prioritize handoff calls at the detriment of new calls. Deriving knowledge from imprecise network data is difficult when developing functional relationships between parameters, requiring advanced modeling techniques with cognitive experience. Soft computing techniques have been shown to handle this challenge given its ability to represent precisely, both data and expert knowledge. An adaptive neuro-fuzzy inference system-based dynamic channel allocation (ANFIS-DCA) framework was proposed to automate the learning of communication parameters for optimized channel allocation decisions. Network parameters considered were received signal strength indication impacted by user mobility, number of guard and general channels, carried traffic, and handoff blocking threshold. The performance of the proposed ANFIS-DCA model was found to outsmart the static FCA and back propagation neural network-based DCA (NN-DCA) schemes using mean square error and root mean square error as performance measures. Our approach can be effectively deployed to improve channel allocation, resource utilization, network capacity, and satisfy users experience.

Advances in Intelligent Systems and Computing, 2021

International Journal of Hybrid Intelligent Systems, 2021

Healthcare systems need to overcome the high mortality rate associated with cardiovascular diseas... more Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it re...

International Journal of Fuzzy Logic Systems, 2019

Interval type-2 fuzzy logic systems (IT2FLSs), have recently shown great potential in various app... more Interval type-2 fuzzy logic systems (IT2FLSs), have recently shown great potential in various applications with dynamic uncertainties. It is believed that additional degree of uncertainty provided by IT2FL allows for better representation of the uncertainty and vagueness present in prediction models. However, determining the parameters of the membership functions of IT2FL is important for providing optimum performance of the system. Particle Swarm Optimization (PSO) has attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving real-world optimization problems. In this paper, a novel optimal IT2FLS is designed, applied for predicting winning chances in elections. PSO is used as an optimized algorithm to tune the parameter of the primary membership function of the IT2FL to improve the performance and increase the accuracy of the IT2F set. Simulation results show the superiority of the PSO-IT2FL to the similar non-optimal IT2FL system with an increase in the prediction.

Journal of Scientific Research and Reports, 2016

The advent of technology has revolutionized systemic approach to issues and methodology in differ... more The advent of technology has revolutionized systemic approach to issues and methodology in different areas of life. Technological apparatus has stepped up human performance and efficiency in transportation, agriculture, entertainment, resource management, training, assessment and other areas of man's endeavour. Specifically, educational assessment has witnessed a shift in paradigm. Since the traditional approaches to examination suffer in areas of security and standard, they are now being replaced in several places with electronic-based methods which have helped human factors in efficient service delivery. Existing electronic-based examination use PIN, password or token for authorization and they are susceptible to different forms of irregularities ranging from impersonation to other related practices. The research reported in this paper focused on the development of a platform that uses fingerprint-based technology for authenticating electronicbased examination takers with a view to improve on security and control. The platform uses suitable mathematical models for fingerprint database, enhancement, feature extraction and pattern matching. A prototype of the platform was subjected to evaluation using fingerprints from different scanners and 500 research subjects. Analysis of results on error rates and matching speed revealed the suitability of the proposed platform.