12 results
Search Results
Now showing 1 - 10 of 12
Article Citation - WoS: 25Identifying Phishing Attacks in Communication Networks Using Url Consistency Features(inderscience Enterprises Ltd, 2020) Azeez, Nureni Ayofe; Salaudeen, Balikis Bolanle; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisPhishing is a fraudulent attempt by cybercriminals, where the target audience is addressed by a text message, phone call or e-mail, requesting classified and sensitive information after presenting himself/herself as a legitimate agent. Successful phishing attack may result into financial loss and identity theft. Identifying forensic characteristics of phishing attack can help to detect the attack and its perpetuators and as well as to enable defence against it. To shield internet users from phishing assaults, numerous anti-phishing models have been proposed. Currently employed techniques to handle these challenges are not sufficient and capable enough. We aim at identifying phishing sites in order to guard internet users from being vulnerable to any form of phishing attacks by verifying the conceptual and literal consistency between the uniform resource locator (URL) and the web content. The implementation of the proposed PhishDetect method achieves an accuracy of 99.1%; indicating that it is effective in detecting various forms of phishing attacks.Article Citation - WoS: 103Citation - Scopus: 160Cassava Disease Recognition From Low-Quality Images Using Enhanced Data Augmentation Model and Deep Learning(Wiley, 2021) Abayomi-Alli, Olusola Oluwakemi; Damasevicius, Robertas; Misra, Sanjay; Maskeliunas, RytisImprovement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.Article Citation - WoS: 23Citation - Scopus: 30Secure Ear Biometrics Using Circular Kernel Principal Component Analysis, Chebyshev Transform Hashing and Bose-Chaudhuri Error-Correcting Codes(Springer London Ltd, 2020) Olanrewaju, L.; Oyebiyi, Oyediran; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, RobertasEar biometrics has generated an increased interest in the domain of biometric identification systems due to its robustness and covert acquisition potential. The external structure of the human ear has a bilateral symmetry structure. Here, we analyse ear biometrics based on ear symmetry features. We apply iterative closest point and kernel principal component analysis with circular kernel for feature extraction while using a circular kernel function, combined with empirical mode decomposition into intrinsic mode functions perceptual hashing using and fast Chebyshev transform, and a secure authentication approach that exploits the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem error-correcting codes to generate 128-bit crypto keys. We evaluate the proposed ear biometric cryptosecurity system using our data set of ear images acquired from 103 persons. Our results show that the ear biometric-based authentication achieved an equal error rate of 0.13 and true positive rate TPR of 0.85.Article Citation - WoS: 11Citation - Scopus: 16Deep Neural Networks for Curbing Climate Change-Induced Farmers-Herdsmen Clashes in a Sustainable Social Inclusion Initiative(Politechnika Lubelska, 2019) Okewu, Emmanuel; Misra, Sanjay; Fernandez Sanz, Luis; Ayeni, Foluso; Mbarika, Victor; Damasevicius, Robertas; Computer EngineeringPeaceful coexistence of farmers and pastoralists is becoming increasingly elusive and has adverse impact on agricultural revolution and global food security. The targets of Sustainable Development Goal 16 (SDG 16) include promoting peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. As a soft approach and long term solution to the perennial farmers herdsmen clashes with attendant humanitarian crisis, this study proposes a social inclusion architecture using deep neural network (DNN). This is against the backdrop that formulating policies and implementing programmes based on unbiased information obtained from historical agricultural data using intelligent technology like deep neural network (DNN) can be handy in managing emotions. In this vision paper, a DNN-based Farmers-Herdsmen Expert System (FHES) is proposed based on data obtained from the Nigerian National Bureau of Statistics for tackling the incessant climate change induced farmers-herdsmen clashes, with particular reference to Nigeria. So far, many lives have been lost. FHES is modelled as a deep neural network and trained using farmers-herdsmen historical data. Input variables used include land, water, vegetation, and implements while the output is farmers/herders disposition to peace. Regression analysis and pattern recognition performed by the DNN on the farmers-herdsmen data will enrich the inference engine of FHES with extracted rules (knowledge base). This knowledge base is then relied upon to classify future behaviours of herdsmen/farmers as well as predict their dispositions to violence. Critical stakeholders like governments, service providers and researchers can leverage on such advisory to initiate proactive and socially inclusive conflict prevention measures such as, people-friendly policies, programmes and legislations. This way, conflicts can be averted, national security challenges tackled, and peaceful atmosphere guaranteed for sustainable development.Article Citation - WoS: 47Citation - Scopus: 66Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications(Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, RobertasWe implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.Article Citation - WoS: 62Citation - Scopus: 78Hybrid Microgrid for Microfinance Institutions in Rural Areas - a Field Demonstration in West Africa(Elsevier, 2019) Ayodele, Esan; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisWe present a hybrid energy microgrid optimization model for a microbank in a remote rural residential area. The model is based on the use of renewable (wind turbines & solar photovoltaic (PV)) and conventional (gasoline generators) energy sources and battery storage systems. We conducted a detailed assessment of a typical microbank's load, residential loads and energy resources in a village called Ajasse-Ipo in Kwara State, Nigeria. We performed the modeling of a hybrid microgrid system, followed by an economic analysis and sensitivity analysis to optimize the hybrid system design. We performed simulations based on the energy resources available (solar PV, wind, gasoline generator & battery energy storage system) to satisfy the energy demands of the microbank, while the excess energy was supplied to meet the demand of the community loads, i.e. water pumping machine and rural home lighting. The results obtained showed that the hybrid system comprising the solar PV/battery/diesel was most techno-economically viable with a Net Present Cost (NPC) and Cost of Energy (COE) of $468,914 and 0.667$/kWh, respectively. Comparing these results with those obtained using analytical methods, the solar PV, battery and converter sizes obtained were slightly higher than the optimal system configurations as produced by HOMER. The proposed hybrid energy system also allowed to achieve almost 50% reductions in CO2, CO, unburned hydrocarbons, particulate matter, SO2 & NO2. The system can be applicable for other rural regions in the developing countries with similar environmental conditions.Article Citation - WoS: 13Citation - Scopus: 18An E-Environment System for Socio-Economic Sustainability and National Security(Politechnika Lubelska, 2018) Okewu, Emmanuel; Misra, Sanjay; Fernandez Sanz, Luis; Maskeliunas, Rytis; Damasevicius, Robertas; Computer EngineeringThough there are adequate institutional frameworks and legal instruments for the protection of the Sub-Saharan African environment, their impact on the development and conservation (protection) of the environment leaves much to be desired. This assertion is substantiated by the reality that inspite of these regulatory frameworks, the environment is largely degraded with negative ramifications for the twin goals of attaining sustainable socio-economic advancement and realization of environmental rights. Both national and regional state of environment (SoE) reports show that degradation is apparent. It is worthy of mention that almost all African countries have ratified and domesticated the various regional and subregional environmental agreement. Efforts to solve the puzzle have revealed that corruption and environmental degradation in Sub-Saharan Africa are closely linked. Financial impropriety in ecological funds management, poorly equipped environmental protection institutions, and inadequate citizens' environmental management awareness campaigns are outcomes of corruption in the public sector. Since corruption thrives in the absence of transparency and accountability, this study proposes a cutting-edge technology-based solution that promotes participatory environmental accountability using an e-Environment system. The web-based multi-tier e-Environment system will empower both citizens and government officials to deliberate online real-time on environmental policies, programmes and projects to be embarked upon. Both parties will equally put forward proposals on the use of tax payers money in the environment sector while monitoring discrepancies between amount budgeted, amount released and actual amount spent. We applied design and software engineering skills to actualize the proposed solution. Using Nigeria as case study, our research methodology comprised literature review, requirements gathering, design of proposed solution using universal modelling language (UML) and development/implementation on the Microsoft SharePoint platform. In view of our determination to evolve a zero-defect software, we applied Cleanroom Software Engineering techniques. The outcome obtained so far has proved that the model supports our expectations. The system is not only practical, but ecologically sound. It is anticipated that the full-scale implementation of such an enterprise e-Environment system will decrease the current tide of corruption in the environment sector, mitigate environmental degradation and by extension, reduce social-economic tensions and guarantee national security.Article Citation - WoS: 11Citation - Scopus: 18Prospects of Ocean-Based Renewable Energy for West Africa's Sustainable Energy Future(Emerald Group Publishing Ltd, 2021) Adesanya, Ayokunle; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, RobertasPurpose The limited supply of fossil fuels, constant rise in the demand of energy and the importance of reducing greenhouse emissions have brought the adoption of renewable energy sources for generation of electrical power. One of these sources that has the potential to supply the world's energy needs is the ocean. Currently, ocean in West African region is mostly utilized for the extraction of oil and gas from the continental shelf. However, this resource is depleting, and the adaptation of ocean energy could be of major importance. The purpose of this paper is to discuss the possibilities of ocean-based renewable energy (OBRE) and analyze the economic impact of adapting an ocean energy using a thermal gradient (OTEC) approach for energy generation. Design/methodology/approach The analysis is conducted from the perspective of cost, energy security and environmental protection. Findings This study shows that adapting ocean energy in the West Africa region can significantly produce the energy needed to match the rising energy demands for sustainable development of Nigeria. Although the transition toward using OBRE will incur high capital cost at the initial stage, eventually, it will lead to a cost-effective generation, transmission, environmental improvement and stable energy supply to match demand when compared with the conventional mode of generation in West Africa. Originality/value The study will contribute toward analysis of the opportunities for adopting renewable energy sources and increasing energy sustainability for the West Africa coast regions.Article Citation - WoS: 19Citation - Scopus: 24Gender Detection Using 3d Anthropometric Measurements by Kinect(Polska Akad Nauk, Polish Acad Sciences, 2018) Camalan, Seda; Sengul, Gokhan; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, RobertasAutomatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.Review Citation - WoS: 16Citation - Scopus: 28A Systematic Literature Review on Compliance Requirements Management of Business Processes(Springer india, 2020) Mustapha, A. M.; Arogundade, O. T.; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisOne crucial aspect that had cost business organizations so much is management of compliance requirements from various regulatory sources. In a bid to avoid being penalized, some organizations have adopted various techniques to accomplish this task. However, literature revealed that few thorough reviews have been centered on this subject in a systematic way. This implies that a review that systematically captured the entire crucial elements such as implementation environment, constraints types addressed, main contributions and strengths of the existing techniques is missing. This has led to the lack of sufficiently good context of operation. A systematic review on existing literatures is presented in this paper, which focuses on the management of business process compliance requirements in order to present summarized evidences and provide a lead-up for appropriately positioning new research activities. The guideline for conducting systematic literature review in software engineering by Kitchenham was employed in carrying out the systematic review as well as a review planning template to execute the review. Results showed that control flow and data flow requirements have been addressed most in recent time. The temporal and resource allocation requirements have been under researched. The approaches that have been employed in business process compliance requirements management are model checking, patterns, semantic, formal, ontology, goal-based requirements analysis and network analysis. The traditional business environment has been put into consideration more than the cloud environment. The summary of research contributions revealed that the approaches have been more of formal techniques compared to model checking and semantics. This shows that there is a need for more research on business process compliance that will be centered on the cloud environment. Researchers will be able to suggest the technique to be adopted based on the combined importance of each criterion that was defined in this work.
