International Journal of Informatics and Communication Technology (IJ-ICT)
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An improved approximate parallel prefix adder for high performance computing applications: a comparative analysis
Get PDFBinary adders are fundamental in digital circuit designs, including digital signal processors and microprocessor data path units. Consequently, significant research has focused on improving adders’ power-delay efficiency. The carry tree adder (CTA) is alternatively referred to as the parallel prefix adder (PPA), is among the fastest adders, achieving superior performance in very large scale integrated (VLSI) implementations through efficient concurrent carry generation and propagation. This study introduces approximate PPAs (AxPPAs) by applying approximations in prefix operators (POs). Four types of AxPPAs approximate kogge-stone, approximate brent-kung, approximate ladner fischer, and approximate sparse kogge-stone-were designed and implemented on FPGA with bit widths up to 64-bit. Delay measurements from static timing analysis using Xilinx ISE design suite version 14.7 indicate that AxPPAs exhibit better latency performance than traditional PPAs. The AxPPA sparse kogge-stone, in particular, demonstrated superior area and speed performance, achieving a delay of 2.501ns for a 16-bit addition
An innovative approach for predictive modeling and staging of chronic kidney disease
Get PDFDiagnosing silent diseases such as chronic kidney disease (CKD) at an early stage is challenging due to the absence of symptoms, making early detection crucial to slowing disease progression. This study addresses this challenge by introducing a novel feature, the estimated glomerular filtration rate (eGFR), calculated using the modification of diet in renal disease (MDRD) formula. We enriched our dataset by incorporating this feature, effectively increasing the volume of data at our disposal. eGFR serves as a critical indicator for diagnosing CKD and assessing its progression, thereby guiding clinical management. Our focus is on developing machine learning and deep learning models for the efficient and precise prediction of CKD. To ensure the reliability of our approach, we employed robust data collection and preprocessing techniques, resulting in refined information for model training. Our methodology integrates various machine learning and deep learning models, including four machine learning algorithms: adaptive boosting (AdaBoost), random forest (RF), Bagging, and artificial neural network (ANN), as well as a hybrid model. Our proposed ANN_AdaBoost model not only introduces a novel perspective by addressing an identified gap but significantly enhances CKD prediction
A system architecture for mixed reality systems in vocational schools in Indonesia
Get PDFIn Indonesia, vocational schools are less favored compared to K -12 schools. Unfortunately, graduates from vocational schools do not fulfill the minimum requirements set by industries, particularly in the current era of industry revolution 5.0. This revolution aims to establish society 5.0, where humans and robots collaborate closely to achieve improved work outcomes. One technique to enhance the proficiency of graduates and prepare them for the workforce is by implementing a mixed-reality system. that will effectively address a multitude of issues and significantly enhance the caliber of graduates and before the implementation of mixed reality (MR) systems, it is necessary to create system architecture diagrams to ensures that the system can be utilized not only in specific schools but also in any vocational school in Indonesia. This study comprises 5 participants, including experts from both the professional and academic fields, who possess extensive knowledge in the domains of metaverse, MR systems, and information systems. The methodology employed in this study draws inspiration from James Martin’s rapid application development (RAD). The result of this study is a validated system architectural diagram, endorsed by experts, which depicts a metaverse-based MR system designed specifically for vocational schools in Indonesi
Finite state machine for retro arcade fighting game development
Get PDFTraditional fighting games are a competitive genre where players engage in one-on-one combat, aiming to reduce their opponent's health points to zero. These games often utilize two-dimensional (2D) graphics, enabling players to execute various character movements such as punching, jumping, and crouching. This research investigates the effectiveness of the finite state machine (FSM) method in developing a combo system for a retro fighting game, focusing on its implementation within the Godot Engine. The FSM method, which structures game behavior through states, events, and actions, is central to the game's control system. By employing the game development life cycle (GDLC) methodology, this study ensures a systematic and structured approach to game design. Special attention is given to the regulation of the combo hit system for the game's protagonist in Brawl Tale. The research culminates in the successful development of the retro fighting game Brawl Tale, demonstrating that the FSM method significantly enhances the fluidity and responsiveness of character movements. The findings suggest that the FSM method is an effective tool for simplifying and improving gameplay mechanics in retro-style fighting games
A hybrid approach of pattern recognition to detect marine animals
Get PDFAcquiring up-to-date knowledge about various animals will have a significant impact on effectively managing species within the ecosystem. Manually identifying animals and their traits continues to be a costly and time-consuming process. The development of a system using the most recent developments in computer vision machine learning was necessary to address the issues of detecting sharks and aquatic species in areas filled with surfers, rocks, and various other potential false positives. In the ocean most of the species are cold-blooded animals hence they cannot be tracked with thermal cameras. Ocean’s dynamic environment affects simple techniques like color separation, intensity histograms, and optical flow. Hence a hybrid approach using convolutional neural network - support vector machine (CNN-SVM) classifier is proposed to perform the pattern recognition. A CNN is employed for feature extraction by using the histogram of gradients value. Subsequently, a SVM classifier is employed to identify and categorise marine species in the vicinity of the seacoast. This serves to notify individuals who engage in swimming activities in the ocean. The suggested model is evaluated against alternative machine learning approaches, and it achieves a superior accuracy of 95% compared to the others
Real-time posture monitoring prediction for mitigating sedentary health risks using deep learning techniques
Get PDFSedentary behavior has become a pressing global public health issue. This study introduces an innovative method for monitoring and addressing posture changes during inactivity, offering real-time feedback to individuals. Unlike our prior research, which focused on post-analysis, this approach emphasizes real-time monitoring of upper body posture, including hands, shoulders, and head positioning. Image capture techniques document sedentary postures, followed by preprocessing with bandpass filters and morphological operations such as dilation, erosion, and opening to enhance image quality. Texture feature extraction is employed for comprehensive analysis, and deep neural networks (DNN) are used for precise predictions. A key innovation is a feedback system that alerts individuals through an alarm, enabling immediate posture adjustments. Implemented in MATLAB, the method achieved accuracy, sensitivity, and specificity rates of 98.2%, 90.7%, and 99.2%, respectively. Comparative analysis with established methods, including support vector machine (SVM), random forest, and K-nearest neighbors (KNN), demonstrate the superiority of our approach in accuracy and performance metrics. This real-time intervention strategy has the potential to mitigate the adverse effects of sedentary behavior, reducing risks associated with cardiovascular and musculoskeletal diseases. By providing immediate corrective feedback, the proposed system addresses a critical gap in sedentary behavior research and offers a practical solution for improving public health outcomes
Advanced control techniques for performance improvement of axial flux machines
Get PDFThe topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications
Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis
Get PDFPredictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients
Factors influencing the integration of web accessibility in Moroccan public e-services
Get PDFGovernments worldwide are increasingly digitizing their services to enhance efficiency, transparency, and accessibility for citizens. Morocco has made significant strides in adopting information and communication technology (ICT) and has implemented various initiatives to promote digital transformation across sectors. However, ensuring that digital content and e-services are accessible to everyone, including people with disabilities, is crucial to building an inclusive digital environment. Against this background, this study, based on a qualitative analysis, explores the main factors influencing the integration of web accessibility in the Moroccan public sector from the perspective of web developers and information technology (IT) managers. Through semi-structured interviews and thematic analysis, the findings reveal key barriers such as limited awareness, training deficiencies, and lack of legal framework and available guidelines. Additionally, the study highlights the need for robust managerial backing and greater collaboration with stakeholders, including people with disabilities. By raising awareness and providing actionable insights, this study offers valuable recommendations for policymakers and moves the field forward, providing a foundation for future strategies to enhance web accessibility in the Moroccan public sector
Connected caregiving: investigating mothers in the era of digital access
Get PDFMothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion