International Journal of Informatics and Communication Technology (IJ-ICT)
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Exploratory data analysis and forecasting of dengue outbreaks in Pangasinan using the ARIMA model
Get PDFDengue fever remains a critical public health concern in tropical countries like the Philippines, with Pangasinan frequently experiencing outbreaks due to favorable environmental conditions for mosquito breeding. Despite ongoing efforts to control the disease, the absence of a reliable forecasting tool limits the ability of health authorities to implement proactive measures. This study developed a forecasting model using the autoregressive integrated moving average (ARIMA) technique, following an initial exploratory data analysis (EDA) to identify trends and patterns in historical dengue case data from 2019 to 2024. The ARIMA model was trained and validated using historical data, capturing seasonal variations and projecting future dengue outbreaks. The evaluation metrics, including mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), indicated that the model achieved an accuracy of approximately 78.3%, suggesting reasonable predictive capability. Forecasts for the year 2025 indicate a potential rise in dengue cases, particularly during peak seasons, aligning with observed historical trends. These predictions offer valuable insights for local health authorities, enabling them to plan targeted interventions, allocate resources efficiently, and mitigate the impact of future outbreaks. The study demonstrates the practical application of time series analysis in public health forecasting and provides a proactive tool tailored for the needs of Pangasinan
DFIG integration with ReLIFT converter for grid-connected systems: ANFIS MPPT control
Get PDFAlthough dispersed generation and non-linear loads provide difficulties for contemporary power systems that depend on power electronics, renewable energy sources (RES) are essential for meeting the worldโs energy demands. This paper provides a unique method for maximum power point tracking (MPPT) in doubly fed induction generators (DFIG) system using an Adaptive network based fuzzy inference system (ANFIS) inference system. The suggested ANFIS MPPT controller adaptively modifies discontinuous control gain to reduce chattering phenomena in the excitation system while preserving the resilience of the closed-loop system. Prior to using a DQ control theory controller for rotor magnitude adjustment to accomplish vector control of active and reactive power, the turbine and DFIG must be modeled. The converter maximizes output current while striving for unity power factor and allowable harmonic content
Optimizing solar energy forecasting and site adjustment with machine learning techniques
Get PDFEstimation of solar radiation is a key task in optimizing the operation of power systems incorporating high levels of photovoltaic (PV) generation. This paper discusses the application of machine learning techniques, namely extreme gradient boosting (XGBT) and random forest (RF), to improve accuracy in the forecasting of solar radiation while adapting for different sites. Utilizing datasets such as meteorological and solar radiation data, the suggested models demonstrate the enhancement of forecasting accuracy by 39% from traditionally applied statistical practices. Along with this, this study also encompasses how endogenous and exogenous factors could be involved in better predictions of solar energy availability. From our findings, XGBT, as well as other machine learning techniques, do enjoy superior performance levels when it comes to the forecasting of solar radiation, which in turn promotes efficient management and potential adaptation of solar energy systems. This study demonstrates how this last generation of algorithms could be applied to noticeably improve the efficiency of solar power forecasting and thereby contribute to more sustainable and reliable energy systems as a byproduct of that
Electrifying the roads using wireless charging solutions for next-gen electric vehicles
Get PDFThis paper outlines a solar charging device designed for electric vehicles (Evs), mitigating the drawbacks of conventional fuel-based transportation and environmental pollution. Because EVs are becoming more and more popular throughout the world, there are more of them on the road. Beyond environmental benefits, EVs offer cost savings by substituting expensive fuel with more economical electricity. The study introduces innovative solutions in EV charging, enabling separate charging stations, continuous motion charging, and wireless charging without external power sources. The communication and system operations are controlled by an ESP8266 controller. This advanced approach eliminates the need for intermittent charging stops, representing a solar-powered wireless charging solution for plug-in EVs in transit. This work underscores the critical importance of addressing energy and environmental sustainability
Practice-based teaching using an AI platform to strengthen faculty competency
Get PDFThis research aimed to i) analyze faculty membersโ knowledge, understanding, and skills in using AI for practice-based teaching enhancement, ii) evaluate factors affecting faculty readiness in integrating AI into teaching processes, and iii) design and develop an AI platform to enhance faculty competency in practice-based teaching. The questionnaire, validated by five experts, was administered to 200 respondents divided into two groups: 100 faculty members from public universities and 100 from private universities. Comparative analysis revealed that public university faculty and private university faculty statistically significant differences in challenges and concerns at the 05 level, with public university faculty expressing higher concerns. Significant differences were found in AI experience and skills, attitudes toward AI use, and challenges and concerns. However, no significant differences were observed in three other areas: AI knowledge and understanding, AI readiness, and belief in AIโs effectiveness for practice-based learning enhancement. Data from both groups were utilized in designing and developing the AI platform to enhance practicebased teaching competency in higher education. Expert evaluation of the platformโs suitability showed high levels of demand for the AI platform and high appropriateness of the technology used in platform development
Detection model for pulmonary tuberculosis and performance evaluation on histogram enhanced augmented X-rays
Get PDFTuberculosis is one of the biggest threats that has been remaining a contagious disease since its discovery, posing a significant risk to millions of lives. Many people yield to TB because of incomplete treatments or the lack of preventive measures. An effective pulmonary TB diagnostic system has remained a big challenge. As it is a contagious disease, it mainly affects the lungs and other vital organs of the human body. We find DL as a subset of ML that runs an incurable disease diagnostic system with multi-neural architectures. In recent ages, a neural model can detect more accurately and quickly resulting in classified labels as normal and positive TB cases.ย ย ย It helps medical practitioners to identify bacterial infections in the early stage. It has also enabled proper diagnosis and treatment for pulmonary tuberculosis. Through this paper, an enhanced detection model to classify TB and non-TB cases using clinical X-ray images has been proposed. The augmented histogram equalized X-rays were applied to top state-of-the-art classifiers. The evaluation matrics have been compared with and without histogram equalization and a comparative study is done to find the best CNN classifiers. The Resnet 50 and ResNet169 have shown the higest accuracy on preprocessed chest X-rays with 99.6% and 99.48% respectively.ย
Exploring diverse perspectives: enhancing black box testing through machine learning techniques
Get PDFBlack box testing plays a crucial role in software development, ensuring system reliability and functionality. However, its effectiveness is often hindered by the sheer volume and complexity of big data, making it difficult to prioritize critical test cases efficiently. Traditional testing methods struggle with scalability, leading to excessive resource consumption and prolonged testing cycles. This study presents an AI-driven test case prioritization (TCP) approach, integrating decision trees and genetic algorithms (GA) to optimize selection, eliminate redundancy, and enhance computational efficiency. Experimental results demonstrate a 96% accuracy rate and a 90% success rate in identifying relevant test cases, significantly improving testing efficiency. These findings contribute to advancing automated software testing methodologies, offering a scalable and efficient solution for handling large-scale, data-intensive testing environments
Securing Defi: a comprehensive review of ML approaches for detecting smart contract vulnerabilities and threats
Get PDFThe rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems
Towards efficient fog computing in smart cities: balancing energy consumption and delay
Get PDFIn this work, we propose fog-based energy-delay optimization (F-EDO) approach and benchmark its performance against the cloud-based energydelay optimization (C-EDO) method, focusing on energy consumption and delay. Unlike previous studies that optimize energy or delay separately, FEDO minimizes both metrics simultaneously, achieving up to 52.2% energy savings with near-zero delay. Additionally, increasing the number of users also leads to energy savings. This is due to the optimized placement of fog servers at the access layer which reduces network energy consumption compared to C-EDO. F-EDO also significantly reduces delay, with negligible delay compared to C-EDO due to fog servers are placed closer to the users which minimized the transmission distances. Besides, the results also show that the energy saving in F-EDO compared to the C-EDO increased as the processing capacity of the processing server increased while maintaining its minimal delay. Overall, F-EDO proves to be a more energyefficient and lower-delay solution for IoT networks, offering a better alternative to cloud-based offloading
High gain multi-layered microstrip patch antenna for x- band applications
Get PDFThis research investigates the development of a multi-stacked microstrip antenna featuring two patch elements positioned in a layered configuration. The antenna design incorporates three substrates with different dielectric constants, separated by an air gap, to evaluate their impact on improving bandwidth and gain. The primary objective of this research is to enhance the efficiency of a microstrip patch antenna by utilising a multilayer substrate structure. Simulation results indicate that stacking substrates with varying dielectric properties significantly enhances antenna performance. The bandwidth increases considerably, from 1.38 GHz to 2.37 GHz, while the peak gain improves from 6.6 dBi to 7.9 dBi. These advancements highlight the antenna's effectiveness in operating within the X-band frequency range, making it suitable for wireless and satellite communication systems. The design and its performance were analysed using high-frequency structure simulator (HFSS) simulation software, which validated its practical feasibility. This innovative configuration addresses the bandwidth limitations typically associated with conventional microstrip antennas, ensuring improved operational efficiency for modern communication technologies. The findings highlight the benefits of utilising a multi-stacked structure to achieve superior antenna performance, particularly in advanced communication applications