Research Articles

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Towards a framework for bias prevention to ensure open data quality
(International Journal Corner, 2024-12-31) Nansukusa, Yudaya; Kalyankolo, Umaru
The rapid growth of open data initiatives has emphasized their potential to enhance transparency, foster innovation, and support equitable decision-making across sectors. However, the quality and reliability of open data remain compromised by biases that alter outcomes and spread inequalities. This paper critically examines the systemic sources of bias, including sampling, annotator, and algorithmic biases, that undermine data integrity and decision-making processes. It proposes a comprehensive framework to mitigate these biases through standardized data management protocols, inclusive data collection practices, robust data stewardship, and cross-sector collaboration. The study also highlights the ethical imperatives and practical challenges of bias prevention, emphasizing the need to balance inclusivity with privacy and resource constraints. By prioritizing fairness, inclusivity, and dependability, the proposed interventions aim to enhance the credibility and societal impact of open data, reaffirming its role as a catalyst for equitable innovation and policy development. The findings underscore that addressing biases in open data is not only a technical necessity but also a moral imperative essential for sustaining its transformative potential.
Model predictive control based on single-phase shift modulation for triple active bridge DC-DC converter
(Springer Nature, 2024-12-05) Adam, Ahmed Hamed Ahmed; Chen, Jiawei; Xu, Minghan; Kamel, Salah; Ali, Guma
The triple-active bridge (TAB) converter is widely used in various applications due to its high efficiency and power density. However, the high-frequency (HF) transformer coupling between the ports presents challenges for controller design. This article presents a model predictive control (MPC) approach based on single-phase shift modulation for the TAB converter. The developed MPC offers improved transient performance, control flexibility, and precision, ensuring compliance with DC voltage regulations and achieving optimal solutions for port decoupling. The MPC utilizes a cost function to provide robust voltage regulation, and an algorithm based on Karush-Kuhn-Tucker (KKT) conditions is developed to derive closed-form solutions for optimal control parameters. To validate the performance of the TAB converter with the proposed MPC control, Typhoon 602 hardware-in-loop (HIL) experimental case study is conducted. Additionally, a comparison with previous works is carried out to confirm the effectiveness of the proposed method. The results of the HIL experimental setup and the comparative analysis demonstrate that the developed method is effective, providing faster dynamic characteristics and port power decoupling operation capability.
A secure and efficient blockchain and distributed ledger technology-based optimal resource management in digital twin beyond 5G networks using hybrid energy valley and levy flight distributer optimization algorithm.
(IEEE, 2024-08-19) Kumar, K. Suresh; Alzubi, Jafar A.; Sarhan, Nadia M.; Awwad, E. M.; Kandasamy, V.; Ali, Guma
This paper aims to establish a virtual object management system, as well as optimal task scheduling using the foundation of Digital Twins (DT), to improve the user’s experience with management and to accomplish the task efficiently. On the other hand, offloading tasks using IoT gadgets to edge computing, fails to speed up control by users. The capabilities of the DT are provided by executing processes such as visualization, virtualization, synchronization, and simulation. The optimal selection of the virtual objects for the DT is done by utilizing the implemented Hybrid Energy Valley with Lévy Flight Distribution Optimization (HEV-LFDO) in order to optimally offload the task by the edge devices. The optimal selection of the virtual objects is done with the aid of the HEV-LFDO in the DT by considering the total cost of executing all tasks using the selected virtual objects and the decision variables to determine whether a virtual object is taken for executing a task or not as the constraint. The data for performing resource management is secured using the blockchain or distributed ledger technology. This accounts for the minimization of the local loss function. Finally, the secured data is considered for optimal resource management tasks. The optimal resource management is done using the same HEV-LFDO. This optimal resource management is carried out by considering the constraints like the cost of assigning a virtual object for the task to the edge device, and the cost of assigning the task to the edge device. These two costs are analyzed by taking the network’s bandwidth, energy consumption, and computational resources into consideration. Experimental verifications are conducted on the executed optimal resource management scheme to prove the ability of the implemented model to be integrated with the edge computing network. The overall processing time as well as the latency are also minimized by executing the optimal resource management scheme.
Harnessing the potential of artificial intelligence in managing viral hepatitis
(Mesopotamian journal of Big Data, 2024-08-15) Ali, Guma; Mijwil, Maad M.; Adamopoulos, Ioannis; Buruga, Bosco Apparatus; Gök, Murat; Sallam, Malik
Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, BMC, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar databases. Peer-reviewed publications written in English between January 2019 and August 2024 were examined. The data of the selected research papers were synthesized and analyzed using thematic and narrative analysis techniques. The use of AI-driven algorithms in viral hepatitis control involves many significant aspects. AI improves diagnostic accuracy by integrating machine learning (ML) models with serological, genomic, and imaging data. It enables tailored treatment plans by assessing patient-specific characteristics and predicting therapy responses. AI-powered technologies aid in epidemiological modeling, and AI-powered systems effectively track treatment adherence, identify medication resistance, and control complications associated with chronic hepatitis infections. It is vital in identifying new antiviral medicines and vaccines, speeding the development pipeline through high-throughput screening and predictive modeling. Despite its transformational promise, using AI in viral hepatitis care presents various challenges, including data privacy concerns, the necessity for extensive and varied datasets, and the possibility of algorithmic biases. Ethical considerations, legal frameworks, and multidisciplinary collaboration are required to resolve these issues and ensure AI technology’s safe and successful use in clinical practice. Exploiting the full AI’s potential for viral hepatitis management provides unparalleled prospects to improve patient outcomes, optimize public health policies, and, eventually, and alleviate the disease’s negative impact worldwide. This study seeks to provide academics, medics, and policymakers with the fundamental knowledge they need to harness AI’s potential in the fight against viral hepatitis.
Practical application and management of information communication technology (ict) to enhance the performance of Ugandan secondary schools in West Nile
(East African Nature and Science Organization, 2025-01-05) Wamusi, Robert; Habibu, Taban
This study focuses on the contribution that Information and Communication Technology (ICT) can make to improving teacher and student performance in secondary schools in West Nile Region, Uganda. Examines the existing ICT implementation, assesses integration and potential benefits issues, and analyses the relationship between ICT usage and learning outcomes. The research is focused on showing that ICT can go global and how it connects Uganda’s secondary schools in West Nile to global education networks. ICT enhances global learning through active participation, innovation, and flexibility through access to international resources and embracing cross-boundary collaboration and virtual interchanges. This has improved student achievement but also endows students and teachers with the competencies to prosper in a globally connected environment. Ugandan schools have poorly developed ICT facilities, but schools embrace ICT education and facilities for operations. The study centers on specific ICT issues in West Nile schools and explores the possibility of using ICT to raise aggregate performance and efficiency in communication, collaboration, and organizational management. Quantitative and qualitative data were collected from 400 respondents, including teachers and students from 10 secondary schools. Data collection tools included questionnaires and interviews. Quantitative analysis was performed using SPSS, while NVivo was used for qualitative analysis. Ethical considerations were strictly adhered to protect participants' rights. While observing ICT integration in teaching across the ten selected secondary schools in West Nile and surveying 100 teachers, researchers found that 55.6% of them sometimes, 33.3% consistently, and 11% seldom integrate ICT in their teaching. This limited integration is due to a lack of ICT equipment, for example, computers, projectors, and internet connections; inadequate teacher education, where the majority of the teachers are found to be either lacking skills or self-confidence to incorporate ICT in teaching; and limited resource availability where even schools that have procured ICT tools are most often found to be having very few that are inadequacy for the needs of both teacher and students to make effective use of. These results raise concerns regarding the existing disparities in developments and funds for ICT training in West Nile’s secondary schools, with recommendations being made to enhance specific plans to reduce the digital divide. These include making ICT tools a focal area, increasing internet connection, and providing training activities that would increase and develop the competencies of the teaching staff. They seek to devise a technological learning atmosphere to enhance education, teacher, and student learning outcomes.
A Comprehensive review on cryptographic techniques for securing internet of medical things: A state-of-the-art, applications, security attacks, mitigation measures, and future research direction.
(Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024-11-30) Wamusi, Robert; Asiku, Denis; Adebo, Thomas; Aziku, Samuel; Kabiito, Simon Peter; Zaward, Morish; Guma, Ali
As healthcare becomes increasingly dependent on the Internet of Medical Things (IoMT) infrastructure, it is essential to establish a secure system that guarantees the confidentiality and privacy of patient data. This system must also facilitate the secure sharing of healthcare information with other parties within the healthcare ecosystem. However, this increased connectivity also introduces cybersecurity attacks and vulnerabilities. This comprehensive review explores the state-of-the-art in the IoMT, security requirements in the IoMT, cryptographic techniques in the IoMT, application of cryptographic techniques in securing the IoMT, security attacks on cryptographic techniques, mitigation strategies, and future research directions. The study adopts a comprehensive review approach, synthesizing findings from peer-reviewed journals, conference proceedings, book chapters, Books, and websites published between 2020 and 2024 to assess their relevance to cryptographic applications in IoMT systems. Despite advancements, cryptographic algorithms in IoMT remain susceptible to security attacks, such as man-in-the-middle attacks, replay attacks, ransomware attacks, cryptanalysis attacks, key management attacks, chosen plaintext/chosen ciphertext attacks, and side-channel attacks. While techniques like homomorphic encryption enhance security, their high computational and power demands pose challenges for resource-constrained IoMT devices. The rise of quantum computing threatens the efficacy of current cryptographic protocols, highlighting the need for research into quantum-resistant cryptography. The review identifies critical gaps in existing cryptographic research and emphasizes future directions, including lightweight cryptography, quantum-resistant methods, and artificial intelligence-driven security mechanisms. These innovations are vital for meeting the growing security requirements of IoMT systems and protecting against increasingly sophisticated threats.
Predictive maintenance (Ai) in power generation for rotating machines based on vibration analysis
(2024) Kalyankolo, Zaina; Mwesigwa, Samuel; Ainomuhwezi, Martha; Kibande, Steven; Kalyankolo, Umar
Predictive maintenance, aided by Artificial Intelligence (AI), has emerged as a game-changing approach that will revolutionize how to manage and maintain machinery especially rotating machinery particularly in power generation equipment. Traditional preventive maintenance approaches have proven to be expensive and time-consuming, and frequently fail to detect possible issues before they occur. Case in point is the Callide Power Station’s Unit C4 incident. In 2021, an offshore platform experienced a catastrophic failure of gas turbine generator due to a sudden bearing failure. The incident took place on May 25, 2021, at the Callide Power Station's Unit C4. This breakdown resulted in substantial damage to the transmission network. The primary cause was discovered as high system vibrations and inadequate maintenance of the lubrication systems, which are critical for the smooth operation of the bearings. This paper presents predictive maintenance (Pd.M.) as a leveraging solution to abrupt failure in rotating machines. The paper focuses on vibration analysis as a major determinant of the equipment health.
Evaluation of corrosion performance of LDX 2101 and UNS S32205 in flexible pipeline applications: A comparative study
(Journal of Engineering Research and Reports, 2024-09-24) Gumoshabe, Michael; Opio, Innocent M.; Makanga, Jackson; Kule, Saimon; Ongom, Douglas
Lean duplex stainless steel (LDSS) has been used in various applications, including flexible pipelines in offshore and other industrial settings. In recent years, LDSS has become the preferred choice over standard duplex stainless steel (DSS) for flexible pipeline applications due to its lower costs, achieved by reducing nickel and molybdenum content, while still providing comparable corrosion resistance and mechanical strength properties to DSS. However, there is still limited reporting on the corrosion effects of reducing these alloys on the behaviour of lean duplex stainless steel in flexible pipelines. This comparative study investigates the corrosion resistance of lean duplex stainless steel, LDX 2101 and duplex stainless steel, UNS S32205 in flexible pipeline applications using linear polarization resistance (LPR). The research focuses on assessing material performance in environments containing CO2 and H2S, commonly found in oil and gas production, by conducting short-term and long-term tests to evaluate pitting and selective corrosion. The samples, LDX 2101 and UNS S32205 were immersed in a 3.5M NaCl solution, and corrosion measurements were performed using the Metrohm Autolab potentiostat. The results indicate that both materials exhibit good corrosion resistance, but there are differences in their performance under specific conditions. While lean duplex stainless steel, LDX 2101, can be used as a substitute for duplex stainless steel UNS S32205, its corrosion resistance and mechanical properties gradually decrease over time due to the reduced nickel and molybdenum content. As a result, it would not be as effective as duplex stainless steel UNS S32205 in withstanding corrosion in aggressive conditions over a prolonged period.
Modeling and implementation of a hybrid solar-wind renewable energy system for constant power supply
(Journal of Engineering, Technology & Applied Science, 2024-08-10) Conceptar, Mubeezi; Kalyankolo, Umaru; Eze, Val Hyginus Udoka; Migisha, Jim; Asikuru, Salama; Nassaga, Musa; Ochima, Noah; Okafor, Wisdom
In recent years, Uganda has significantly increased the use of renewable energy sources, particularly solar and wind power. These energy sources are especially crucial in rural and remote areas where connecting to the national grid is challenging. Renewable Energy Sources (RES) have proven to be cost-effective alternatives to traditional energy sources, which often require substantial investments in transmission and distribution networks. This study focuses on designing and implementing a hybrid renewable energy system that integrates both solar and wind power. The research successfully established a reliable and continuous power supply for the community through the combination of wind and solar energy. The hybrid power generation system operates by simultaneously monitoring solar and wind energy using an ACS712 current and voltage sensor. Controlled by a microcontroller, the system employs dual-channel relay switches to activate the power source with sufficient energy to charge the battery. The programming for this system was conducted using C++ and Arduino software. This study highlights the vast potential within the field of sustainable energy. With rapid and economical electricity production, this hybrid system paves the way toward a greener future, where our energy needs can be met in an environmentally friendly manner.
A survey on artificial intelligence in cybersecurity for smart agriculture: state-of-the-art, cyber threats, artificial intelligence applications, and ethical concerns.
(Mesopotamian Academic Press, Imam Ja'afar Al-Sadiq University, 2024-07-20) Ali, Guma; Mijwil, Maad M.; Buruga, Bosco Apparatus; Abotaleb, Mostafa; Adamopoulos, Ioannis
Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.
Design and implementation of a DC to AC power electronics-based inverter that produces pure sine wave output for critical engineering applications
(International Journal of Recent Technology and Applied Science, 2024-02-17) Mubeezi, Conceptar; Kalyankolo, Umaru; Eze, Val Hyginus Udoka; Okafor, Wisdom O.
Power inverters play a crucial role in the field of engineering, particularly in applications where power stability is imperative. In devices such as Uninterruptible Power Supplies (UPS), the conversion of raw power to DC, subsequent filtering, and inversion to AC are executed through pure sine wave inverters. These inverters exhibit remarkable stability, making them ideal for powering sensitive equipment like data switches and Remote Terminal Units (RTUs). This study delves into the intricate process of converting DC power into a pristine sine wave signal. The heart of this power conversion lies in the utilization of the KA3525A integrated circuit (IC) in conjunction with MOSFETs of the PN55 series, supported by capacitors and resistors for effective power filtration. The KA3525A, a monolithic IC, encompasses all essential control circuits for a pulse width modulating regulator. Within this IC, a voltage reference, error amplifier, pulse width modulator, oscillator, under-voltage lockout, soft start circuit, and output driver collaborate seamlessly. The MOSFETs function as switches, synchronized with the oscillating signal from the KA3525A IC. This coordination, combined with the filter and other signal conditioning units, enables the conversion process. The design achieves the conversion of raw power into a stable pure sine wave signal of 170V AC at the H-bridge terminals, demonstrating the success of the designed approach.
Voltage optimization on low voltage distribution transformer zones using batteries in Uganda
(Journal of Engineering, Technology & Applied Science, 2024-03-07) Kelechi, Edema Simon Iddi; Kalyankolo, Umaru; Eze, Val Hyginus Udoka; Asikuru, Salama; Nassaga, Musa; Ochima, Noah
In the context of Uganda's rapidly growing energy demands and the need for sustainable solutions, this study explores the implementation of voltage optimization techniques in Low Voltage (LV) distribution transformer zones. The research focuses on the innovative integration of batteries to optimize voltage levels, thereby enhancing the efficiency and reliability of the electrical distribution system. By analyzing real-time data from various LV transformer zones in Uganda, this study investigates the impact of voltage fluctuations on the overall power distribution network. The research methodology involves the design and deployment of battery energy storage systems (BESS) strategically placed within LV distribution transformer zones. These BESS units are utilized to store excess energy during periods of low demand and release it during peak hours, ensuring consistent voltage levels and minimizing losses in the distribution network. The study evaluates the effectiveness of this approach through extensive simulations and on-site experiments, considering factors such as battery capacity, charging/discharging rates, and load variations. A comprehensive cost-benefit analysis is conducted to evaluate the potential financial savings and environmental impact associated with this sustainable energy solution. The findings of this research indicate significant improvements in voltage regulation, reduced system losses, and enhanced reliability in LV distribution transformer zones. Additionally, the study demonstrates the feasibility of integrating batteries into the existing infrastructure, thereby contributing to the optimization of the energy distribution system in Uganda. The outcomes of this research provide valuable insights for policymakers, utility companies, and researchers, emphasizing the importance of embracing innovative technologies to address the energy challenges faced by developing nations like Uganda.
Design and implementation of a fire detection, alarm and suppression system using programmable logic controller (PLC)
(International Journal of Academic Engineering Research (IJAER), 2023-12) Kalyankolo, Umaru; Drici, Joseph Felix Tartisious
Industrial and or domestic safety is as much important as the processes carried out in any industry/homes. This is because there is always a tendency for fires to occur due to the existence of fire hazards in industries, domestic and residential settings. Therefore, firefighting system is one of the most important systems in industries and in buildings with multiple occupancy as it protects the facilities, equipment and people against disastrous effects of fire breakouts. Fire detection, alarm and fighting system is a combination of number of devices working together to detect and warn the people through visual and audible appliances when smoke, heat and/or fire are present. It also triggers the suppression system. The alarm used in such system may be activated from flame or smoke detectors and heat detectors. In this project, smoke detectors have been used to detect fire and give an input signal to the programmable logic controller(PLC) which triggers the fire alarm and fire suppression system. Fire alarm system plays the main role in maintaining and monitoring the safety in all kinds of environments and situations. The main objective of this Fire Alarm Control System in Building Automation Using PLC is to make a fire control and suppression system with high reliability and low cost. The system has been designed to cover three zones of protection (three rooms) in which on detection of fire, zone 1 produces audible (buzzer) and visual light emitting diode(LED) alarm, while the LED, direct current (DC) water pump and a buzzer are triggered in zone 2 and a LED, buzzer and Solenoid valve are triggered for zone 3.
Mobile disinfectant spraying robot and its implementation components for virus outbreak: Case study of COVID-19
(International Journal of Artificial Intelligence, 2023-07-28) Udoka, Eze Val Hyginus; Edozie, Enerst; Musika, Davis; Twijuke, Dickens; Wantimba, Janat; Okafor, Wisdom O.; Kalyankolo, Umaru; Nafuna, Ritah; Nansukusa, Yudaya
The virus pandemic COVID-19 outbreak brought a huge pressure to the public healthcare system worldwide, especially in developing African countries like Uganda. The Educational system and institutions were put on a standstill due to no quick countermeasures to make the environment clean and safe for normal activities to continue. This paper successfully and comprehensively reviewed the Bluetooth and smart disinfectant spraying robot that successfully controlled the spread of the deadly virus. It also detailed different components that made up the complete spraying robot systems and from this it was observed that spraying robot systems are made up of almost the same components for implementations but differs on program that is embedded on the microcontroller due to different functions. This programing differs based on the functions that the designer/programmer wants the robot to do despite using almost the same components. This research review paper will act as guide for future researchers when designing and implementing a mobile spraying robot.
Cybersecurity for sustainable smart healthcare: State of the Art, taxonomy, mechanisms, and essential roles
(Mesopotamian Journal of CyberSecurity, 2024-05-23) Ali, Guma; Mijwil, Maad M.
Cutting-edge technologies have been widely employed in healthcare delivery, resulting in transformative advances and promising enhanced patient care, operational efficiency, and resource usage. However, the proliferation of networked devices and data-driven systems has created new cybersecurity threats that jeopardize the integrity, confidentiality, and availability of critical healthcare data. This review paper offers a comprehensive evaluation of the current state of cybersecurity in the context of smart healthcare, presenting a structured taxonomy of its existing cyber threats, mechanisms and essential roles. This study explored cybersecurity and smart healthcare systems (SHSs). It identified and discussed the most pressing cyber threats and attacks that SHSs face, including fake base stations, medjacking, and Sybil attacks. This study examined the security measures deployed to combat cyber threats and attacks in SHSs. These measures include cryptographic-based techniques, digital watermarking, digital steganography, and many others. Patient data protection, the prevention of data breaches, and the maintenance of SHS integrity and availability are some of the roles of cybersecurity in ensuring sustainable smart healthcare. The long-term viability of smart healthcare depends on the constant assessment of cyber risks that harm healthcare providers, patients, and professionals. This review aims to inform policymakers, healthcare practitioners, and technology stakeholders about the critical imperatives and best practices for fostering a secure and resilient smart healthcare ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, healthcare management, and sustainability research. Understanding the most recent cybersecurity measures is critical for controlling escalating cyber threats and attacks on SHSs and networks and encouraging intelligent healthcare delivery.
Innovative Livestock: A Survey of artificial intelligence techniques in livestock farming management
(Wasit Journal of Computer and Mathematics Science, 2023-12-30) Mijwil, Maad M.; Adelaja, Oluwaseun; Badr, Amr; Ali, Guma; Buruga, Bosco Apparatus; Pudasaini, Pramila
Modern technology has recently become a meaningful part of all life sectors, as software, sensors, smart machines, and expert systems are successfully integrated into the physical environment. This technology relies in its work on artificial intelligence techniques to make the right decisions at the right time. These technologies have a significant role in improving productivity, product quality, and industry outputs by significantly reducing human labour and errors that humans may cause. Artificial intelligence techniques are increasingly being integrated into animal husbandry and animal revolution management because they provide advantages and means that serve agriculturalists. These techniques monitor the emotional state of animals, milk production and herd management, feeding habits, the movement of animals, and their health status. AI-powered sensors can monitor the health of livestock and detect early signs of illness or stress to which they are exposed. Also, these techniques contribute to assisting agriculturalists in customising feeding programs, reducing waste, and improving product quality. This article will discuss the role of artificial intelligence techniques in animal control, farm management, disease surveillance, and sustainable resource optimisation practices.
Comparative analysis of PWM AC choppers with different loads with and without neural network application.
(Wasit Journal of Computer and Mathematics Science, 2023-09) Bounab, Mariem; Ali, Guma
In this paper, we focus on the "Artificial Neural Network (ANN) based PWM-AC chopper". This system is based on the PWM AC chopper-encouraged single-phase induction motor. The main purpose of this paper is to design and implement an ideal technique regarding speed control. Here analyzed PWM-based AC-AC converter with resistive load, R-L load and finally, the PWM AC chopper is fed to single phase induction for speed control. Using other soft computing and optimization techniques such as Artificial Neural Networks, Fuzzy Logic, Convolution algorithm, PSO, and Neuro Fuzzy can control the Speed. We used Artificial Neural Network to control the Speed of the PWM-AC Single phase induction motor drive. The Neural Network toolbox has been further used for getting desired responses. Neural system computer programs are executed in MATLAB. The performance of the proposed method of ANN system of PWM AC Chopper fed single phase induction motor drive is better than other traditional and base methods for controlling the Speed, based on the MOSFET.
A Comprehensive review on cybersecurity issues and their mitigation measures in FinTech
(Al-Iraqia Univeristy, 2024-06-10) Ali, Guma; Mijwil, Maad M.; Buruga, Bosco Apparatus; Abotaleb, Mostafa
The fourth industrial revolution has seen the evolution and wide adoption of game-changing and disruptive innovation, "financial technologies (FinTech), around the globe. However, the security of FinTech systems and networks remains critical. This research paper comprehensively reviews cybersecurity issues and their mitigation measures in FinTech. Four independent researchers reviewed relevant literature from IEEE Xplore, ScienceDirect, Taylor & Francis, Emerald Insight, Springer, SAGE, WILEY, Hindawi, MDPI, ACM, and Google Scholar. The key findings of the analysis identified privacy issues, data breaches, malware attacks, hacking, insider threats, identity theft, social engineering attacks, distributed denial-of-service attacks, cryptojacking, supply chain attacks, advanced persistent threats, zero-day attacks, salami attacks, man-in-the-middle attacks, SQL injection, and brute-force attacks as some of the significant cybersecurity issues experienced by the FinTech industry. The review paper also suggested authentication and access control mechanisms, cryptography, regulatory compliance, intrusion detection and prevention systems, regular data backup, basic security training, big data analytics, use of artificial intelligence and machine learning, FinTech regulatory sandboxes, cloud computing technologies, blockchain technologies, and fraud detection and prevention systems as mitigation measures for cybersecurity issues. However, tackling cybersecurity issues will be paramount if FinTech is to realize its full potential. Ultimately, this research will help develop robust security mechanisms for FinTech systems and networks to achieve sustainable financial inclusion.
Artificial intelligence in corneal topography: A short article in enhancing eye care
(Mesopotamian Journal of Artificial Intelligence in Healthcare, 2023-06-17) Ali, Guma; Eid, Marwa M.; Ahmed, Omar G.; Abotaleb, Mostafa; Alaabdin, Anas M. Zein; Buruga, Bosco Apparatus
The eye is a critical part of the human being, as it provides complete vision and the ability to receive and process visual details, and any deficiency in it may affect vision and loss of sight. Corneal topography is one of the essential diagnostic tools in the field of ophthalmology, as it can provide important information about the cornea and the problems that appear in it. Artificial intelligence strategies contribute to the development of the healthcare domain through a group of approaches that have a significant and vital impact on improving the field of ophthalmology. The primary purpose of this paper is to highlight the efficiency of artificial intelligence in extracting features from corneal topography and how these techniques contribute to helping ophthalmologists diagnose corneal topography. Furthermore, the focus is on the performance of AI algorithms, their diagnostic capabilities, and their importance in helping physicians and patients. The effects of this paper confirm the effectiveness and efficiency of artificial intelligence algorithms in the clinical diagnosis of various eye concerns.
Design a hybrid approach for the classification and recognition of traffic signs using machine learning
(Wasit Journal of Computer and Mathematics Science, 2023-07) Ali, Guma; Sadıkoğlu, Emre; Abdelhak, Hatim
Advanced Driver Assistance Systems (ADAS) are a fundamental part of various vehicles, and the automatic classification of traffic signs is a crucial component. A traffic image is classified based on its recognizable features. Traffic signs are designed with specific shapes and colours, along with text and symbols that are highly contrasted with their surroundings. This paper proposes a hybrid approach for classifying traffic signs by combining SIFT with SVM for training and classification. There are four phases to the proposed work: pre-processing, feature extraction, training, and classification. A real traffic sign image is used for classification in the proposed framework, and MATLAB is used to implement the framework

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