Individual Solutions Research Articles

Evaluating Image Processing Capabilities in the Cloud: A Comparative Study of Microsoft Azure and Google Cloud

Cloud computing is a transformative paradigm that enables the distribution of processing power, application execution, and storage across networks of remote computer systems. This model allows for the flexible allocation and release of IT resources over the internet, offering an affordable solution for both businesses and individuals. Through cloud services, users can access a variety of offerings, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Desktop as a Service (DaaS), with pricing based on actual usage. In an increasingly competitive market with diverse service options, selecting a long-term cloud provider can be challenging. Dominant providers like Microsoft Azure and Google Cloud lead this market. This paper provides an in-depth evaluation of the image processing services offered by these providers, focusing on Azure Custom Vision, Azure Computer Vision, Azure Cognitive Services, Google Cloud Vision API, and AutoML Vision. The analysis explores the performance and capabilities of these services, emphasizing their strengths and leadership in cloud technology. The primary goal of this study is to offer a comparative analysis of Azure and Google Cloud, helping organizations and users make informed decisions that align with their long-term objectives. In addition, the paper examines the security measures implemented for Integration Platform as a Service (iPaaS) on both platforms, providing a detailed review of their security features and protective mechanisms. The study also highlights key parameters such as performance, scalability, usability, cost, and security to assist organizations in choosing the most appropriate platform for their specific requirements. Case studies and emerging trends in cloud-based image processing are also covered

СОВЕРШЕНСТВОВАНИЕ ТАМОЖЕННЫХ УСЛУГ В УСЛОВИЯХ ИНТЕЛЛЕКТУАЛЬНЫХ ПУНКТОВ ПРОПУСКА

The paper presents the results of an analysis of the development of intelligent checkpoint technologies in the context of their potential impact on the development of customs services at an intelligent checkpoint. The analysis of the current state of research aimed at improving the efficiency of checkpoints shows the presence of only individual functional solutions that pointwise implement fragmented tasks of instrumental control at checkpoints outside of complex solutions that integrate well-known technical solutions and select and implement advanced technologies of instrumental control in the interests of ensuring the tasks solved by customs and other state regulatory authorities at checkpoints across the state border.

Assistive Communication Web App: A Multi-Model Solution for Individuals with Disabilities”

The Assistive Communication Web App is an innovative platform designed to address the communication challenges faced by individuals with speech, hearing, and motor impairments. By integrating advanced technologies such as speech recognition, head-gaze tracking, and gesture-based sign language recognition, the app provides real-time speech-to-text, text-to-speech, and gesture-to-text functionalities. This research outlines the app's system architecture, methodology, and user interface, emphasizing its potential to enhance accessibility and empower users with greater independence. Through its multi-modal approach, the web app fosters inclusivity and sets a new benchmark for assistive technologies. Keywords: Assistive communication, Speech-to-text, Text-to-speech, Sign language recognition, Head gaze tracking, WebGazer.js, Real-time interaction, Gesture recognition, MediaPipe FaceMesh, TensorFlow HandPose, Web Speech API, Real-time transcription, Multilingual support, Cross-platform compatibility, Emotion detection, User-centered design, Modular architecture

Parents’ initiative in community-based support for children with intellectual and developmental disabilities: The Ahada non-profit model

ABSTRACT Background The birth of a child with an intellectual or developmental disability inherently presents challenges to parents regarding the child’s long-term future. This qualitative study examined the perceptions of parents who are kibbutz members, focusing on the non-profit organisation they established and the socioeconomic model they developed to ensure the future wellbeing of their children with intellectual and developmental disabilities. Method In-depth interviews were conducted with 12 participants and used a thematic analysis based on case study methodology. Results The socioeconomic model developed by kibbutz parents helps alleviate their concerns, but also gave rise to challenges related to kibbutz privatisation and the requirement for programs to be tailored to individual needs. The study highlighted how cooperation among families, the community, and government institutions can contribute to individual and collective solutions for people with disabilities. Conclusion These insights expand our understanding of parental social entrepreneurship within intentional communities and other contexts.

To cool or not to cool: understanding and improving cooling centre use in metro vancouver through community-based participatory action research

Abstract Extreme heat events present significant health risks, particularly for populations facing systemic marginalization. Through interviews and focus groups with 63 (n=60 residents; n=3 service providers) participants, this paper explores the barriers and opportunities related to access and use of cooling centres from both user and provider perspectives, as well as broader implications for addressing vulnerability to extreme heat in the metro Vancouver region of British Columbia, Canada. Initially focused on cooling centres, our research expanded to include the complexities of individual and systemic responses to heat, highlighting the complex interplay between personal preferences and structural barriers. Key barriers to cooling centre usage reported by participants included concerns over safety, hygiene, stigma, lack of information, and infrastructural challenges such as transportation. Service providers faced constraints with staffing, limited hours, and underutilization of additional services. Tensions emerged between individual, do-it-yourself (DIY) approaches and collective solutions, with participants seeking agency in their choices while acknowledging the need for improved, community-based interventions. The study also uncovered the unintended effects of inclusivity, as the presence of individuals who use substances or are unhoused in cooling spaces deterred some potential users, underscoring the challenges of designing universally accessible services. Broader systemic issues, such as housing security and the quality of indoor environments, further intersected with vulnerability to extreme heat, exacerbating risks for those opting to stay at home. The paper recommends leveraging existing public spaces, enhancing social networks, improving tenant protections, and fostering ‘right to cool’ initiatives to balance immediate relief with long-term systemic reform, particularly around the rental housing crisis and tenant insecurity. By addressing both individual and collective needs, this approach can mitigate the health impacts of extreme heat while promoting greater equity and resilience.

Virtual Human Retina: Simulating Neural Signalling, Degeneration, and Responses to Electrical Stimulation.

Current brain-based visual prostheses pose significant challenges impeding adoption such as the necessarily complex surgeries and occurrence of more substantial side effects due to the sensitivity of the brain. This has led to much effort toward vision restoration being focused on the more approachable part of the brain - the retina. Here we introduce a novel, parameterized simulation platform that enables study of human retinal degeneration and optimization of stimulation strategies. The platform bears immense potential for patient-specific tailoring and serves to enhance artificial vision solutions for individuals with visual impairments. Our virtual retina is developed using the software package, NEURON. This virtual retina platform supports large-scale simulations of over 10,000 neurons whilst upholding strong biological plausibility with multiple important visual pathways and detailed network properties. The comprehensive three-dimensional model includes photoreceptors, horizontal cells, bipolar cells, amacrine cells, and midget and parasol retinal ganglion cells, with comprehensive network connectivity across various eccentricities (1 mm to 5 mm from the fovea) in the human retina. The model is constructed using electrophysiology, immunohistology, and optical coherence tomography imaging data from healthy and degenerate human retinas. We validated our model by replicating numerous experimental observations from human and primate retina, with a particular focus on retinal degeneration. We simulated interactions between diseased retinas and state-of-the-art retinal implants, shedding light on the limitations of commercial retinal prostheses. Our results suggested that appropriate stimulation settings with intraretinal prototype devices could leverage network-mediated activation to achieve activation mosaics more alike that of the retina's response to natural light, promoting the prospect of more naturalistic vision. Our study additionally highlights the importance of controlling inhibitory circuits in the retinal network to induce functionally relevant retinal activity. This study demonstrates the potential of this software package and highlights its utility as a valuable tool for engineers, scientists, and clinicians in the design and optimisation of retinal stimulation devices for both research and educational applications.

Integrating Expense Tracking with AI-Driven Investment Recommendations

The Expense Tracker is a comprehensive financial management application developed using the MERN stack—MongoDB, Express.js, React, and Node.js—to simplify expense tracking, budgeting, and investment decision-making. The application features a secure backend powered by Node.js and Express.js, utilizing RESTful APIs for efficient data handling, while MongoDB ensures flexible and scalable data storage. The React-based front end provides a responsive and intuitive interface, enabling users to seamlessly input, categorize, and visualize their expenses. Core functionalities include user authentication for secure and personalized access, secure expense logging to maintain accurate financial records, category management and budget tracking for effective financial organization, and interactive charts offering real-time insights into spending patterns. A key innovation of the application is the PennyDrop feature, which leverages user income and expense data to provide personalized investment suggestions. Using data-driven algorithms, PennyDrop empowers users to make informed financial decisions by aligning their income with tailored investment opportunities. This integration of expense tracking with actionable investment insights creates a holistic platform for financial management. The application is designed to be scalable, maintainable, and user-friendly, making it an ideal solution for individuals and businesses seeking an efficient tool for managing their finances and optimizing their investments. Index Terms— Budget Tracking, Category Management, Data-Driven Investment Insights, Expense Categorization, Expense Input, Expense Logging, Financial Management Application, Income-Based Investment Suggestions, Interactive Charts, MERN, PennyDrop Feature, Personalized Financial Solutions, Real-Time Financial Insights, RESTful APIs, Scalable Data Storage.

A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.

Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities. Despite this promise, extracting reliable control signals from noisy brain data remains a critical challenge. In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. Our method demonstrates exceptional performance, achieving an accuracy of 79.44% on the BCI Competition IV 2a dataset, surpassing existing state-of-the-art techniques in using multiple CNN models. This advancement offers significant promise for enhancing the efficacy and versatility of BCIs in a wide range of real-world applications, from assistive technologies to neurorehabilitation, thereby providing robust solutions for individuals with motor disabilities.

Exploring Moral Distress, Related Factors and Coping in Emergency Nurses: A Mixed Method Study.

To determine emergency nurses with moral distress level, related factors and coping. This is a mixed-methods study which included quantitative and qualitative approaches. In quantitative phase, surveys were completed to 252 emergency nurses, while in the qualitative phase, semi-structured interviews were conducted with 23 nurses. Descriptive analysis was used for quantitative data and thematic analysis for qualitative data. The related factors of moral distress in nurses were as follows; educational status, work experience, satisfaction, and employment status. EN usually uses positive reinterpretation and development to cope with problems and tried to solve moral distress by getting own individual solutions and getting support from colleagues, friends and families due to lack of support from managers. In order to improve healthy working environment and conditions, it is important to create plans and strategies for EDs. It is recommended to develop practical and structural strategies and interventions that effective coping with moral distress by hospital managers. It will increase the quality of care provided by nurses and patient satisfaction.

Heart Attack Prediction

Cardiovascular diseases, including heart attacks, remain a leading cause of mortality globally. Early prediction and intervention play a critical role in preventing and managing such conditions. This project presents a comprehensive approach to heart attack prediction through the integration of machine learning techniques and a user-friendly graphical user interface (GUI) implemented in Python. The system leverages a dataset of relevant health parameters, including age, blood pressure, cholesterol levels, and other key factors. A machine learning model, trained on historical data, is employed to analyze and predict the likelihood of a heart attack based on these input features. The model's accuracy and efficiency are crucial to its effectiveness, and various algorithms are explored to identify the optimal solution. To enhance accessibility and usability, a GUI is developed using Python's Tkinter library. The GUI enables users to input their health parameters easily and receive an instant prediction regarding their potential risk of a heart attack. The visual representation of the prediction, along with additional informative features, aims to empower individuals to take proactive measures towards a healthier lifestyle. The project not only contributes to the field of cardiovascular health prediction but also serves as an educational tool for users to better understand the factors influencing their cardiovascular well-being. The integration of machine learning into a userfriendly GUI provides a practical and efficient solution for both healthcare professionals and individuals concerned about their heart health.

Breath Analyzer for Real-Time Exercise Fat Burning Prediction: Oral and Alveolar Breath Insights with CNN.

The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fat metabolism. In this study, we developed an advanced breath analyzer system designed to provide real-time monitoring of exercise-induced fat burning by analyzing volatile organic compounds (VOCs) present in both oral and alveolar breath. Acetone in exhaled breath and β-hydroxybutyric acid (BOHB) in the blood are both biomarkers closely linked to the metabolic fat burning process occurring in the liver, particularly after exercise. The breath analyzer utilizes a sensor array to detect VOC patterns, with the data analyzed using a one-dimensional convolutional neural network (1D CNN) for an accurate prediction of BOHB levels in the blood. We collected and analyzed 30 exhaled breath samples with our analyzer and blood samples for BOHB from participants before and after exercise. The results showed a strong correlation between sensor responses and BOHB levels, with Pearson correlation coefficients of 0.99 across different postexercise time points. The 1D CNN model effectively estimated BOHB concentrations, achieving Pearson coefficients of 0.96 for the training data set and 0.86 for the test data set. Additionally, our findings confirm that alveolar air samples, which contain metabolic byproducts from deeper in the lungs, offer more reliable data for fat burning analysis than oral air samples. This noninvasive, real-time breath monitoring tool offers a promising solution for individuals demanding to optimize their exercise routines and track metabolic health with high precision and accuracy.

Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review.

Some previous studies have focused on using physiological signals to detect stress in individuals with ASD through wearable devices, yet few have focused on how to design such solutions. Wearable technology may be a valuable tool to aid parents and caregivers in monitoring the emotional states of individuals with ASD who are at high risk of experiencing very stressful situations. However, effective wearable devices for individuals with ASD may need to differ from solutions for those without ASD. People with ASD often have sensory sensitivity and may, therefore, not tolerate certain types of accessories and experience discomfort when using them. We used the Scopus, PubMed, WoS, and IEEE-Xplore databases to search for studies published from 2014 to 2024 to answer four research questions related to wearable solutions for individuals with ASD, physiological parameters, and techniques/processes used for stress detection. Our review found 31 articles; not all studies considered individuals with ASD, and some were beyond the scope of this review. Most of the studies reviewed are based on cardiac activity for stress monitoring using photoplethysmography (PPG) and electrocardiography (ECG). However, limitations include small sample sizes, variability in study conditions, and the need for customization in stress detection algorithms. In addition, there is a need to customize the stress threshold due to the device's high individual variability and sensitivity. The potential of wearable solutions for stress monitoring in ASD is evident, but challenges include the need for user-friendly and unobtrusive designs and integrating these technologies into comprehensive care plans.

Effect of Degree of Substitution and Polymer Ratio on the Structure of Chitosan: Carboxymethyl Starch (Bio)Polyelectrolyte Complexes.

In this work, three carboxymethyl starches (CMS) were obtained by the two-step reaction process of carboxymethylation with different degrees of substitution (0.16, 0.33, and 0.36). From these samples, (bio)polyelectrolyte complexes ((bio)PECs) were obtained with chitosan (Chit) by the mixing of individual solutions of polymers (0.25 wt.%) at different volume ratios. The effect of the biopolymer and ionized groups of z ratios, pH, and degree of substitution of CMS in the formation of PEC were evaluated by turbidimetry and dynamic light scattering. The results showed that increasing the amount of CMS samples (ratio of z) led to an increase in the efficiency of the formation of (bio)PEC using CMS with a high DS value. Using the turbidimetry method for the chitosan and CMS mixtures, it was observed that the formation of (bio)PEC is divided into four transition zones delimited by pH transition points, and the stoichiometric complexation (z = 1) is achieved at a pH that displayed morphological changes "pHmorph", which is a single point for Chit:CMS 1, and for Chit:CMS 2 and Chit:CMS 3, this is a range of 4.9-6.4 and 4.3-6.4, respectively. Analysis of the structural properties of the structures of (bio)PECs by dynamic light scattering was characterized by monomodal distribution, and the main observed effect was associated with an increase in the value of Davg with an increase in the ratio of Chit:CMS.

Revealing the Adverse Potential of Six SARS-CoV-2 Antivirals by Aliivibrio fischeri Assay: Toxicity Analysis of Single Agent Solutions and Binary Mixtures

The COVID-19 pandemic has intensified the development of new antiviral agents specifically intended for the SARS-CoV-2 virus, but has also increased the use of some already known antiviral agents originally intended for other viruses. Although the pandemic has ended, the SARS-CoV-2 virus is expected to be present in the human population forever, as is the case with the influenza virus, for example. Such a scenario guarantees the continued use of SARS-CoV-2 antivirals and, accordingly, their continued release into the environment. Unfortunately, there is little or no information on the adverse potential of most of these antiviral agents. In this study, the acute toxicity of six antiviral agents used in the treatment of SARS-CoV-2 infections was determined. These are atazanavir, ribavirin, emtricitabine, nirmatrelvir, sofosbuvir and oseltamivir, sorted according to their toxicity, starting with the most toxic agent. Toxicity was determined using the marine bacterium Aliivibrio fischeri according to the ISO 11348-1:2007 standard. In addition to the toxicities of the individual antiviral solutions, the toxicities of binary antiviral mixtures were also determined. By comparing the experimentally determined toxicities of the mixtures with the values estimated by the concentration addition model and the independent action model, we analyzed the mode of joint toxic activity of these antiviral agents. Additive behavior was observed for most binary combinations. The combination of nirmatrelvir and sofosbuvir led to an antagonistic deviation from the concentration addition model, while a synergistic deviation was observed for the combinations of emtricitabine with atazanavir and with nirmatrelvir, as well as for the combinations of ribavirin with atazanavir, oseltamivir and sofosbuvir. All tested binary combinations showed a synergistic deviation from the independent action model.

The Impact of Orthodontic Treatment on Body Weight Due to Change in Dietary Habits.

Background Orthodontic treatment, while primarily focusing on correcting dental alignment and occlusion, has been increasingly validated for its potential impact on broader aspects of oral health and general well-being: its potential influence on body weight. While the mechanical effects of orthodontic appliances are well documented in the literature, their potential behavioral impact on weight loss remains underexplored. Beyond its primary role in correcting dental alignment, our study has unveiled a lesser-known benefit: its potential to aid in weight reduction among individuals who have already struggled through conventional methods. Orthodontic treatment is a surprising yet successful intervention in the field of weight management. Often known only for its use in correcting dental malalignments, our research has revealed an additional benefit: it can help people who have struggled to do so with other conventional methods lose weight. Orthodontic treatment accomplishes the same goals more quickly and with few side effects. In addition to highlighting the aesthetic and functional benefits of orthodontics, this growing body of knowledge also presents orthodontics as a novel solution for individuals struggling with weight issues. In exploring the mutually beneficial relationship between orthodontic care and weight loss, we looked at a way to achieve both improved overall health and fitness along with dental alignment. Since obesity is associated with a notably higher risk of negative health outcomes, losing weight not only helps you feel better about yourself but also improves your general health and wellness. This study explores the relationship between orthodontic treatment and reduction in weight among patients undergoing treatment at Sardar Begum Dental College. Its primary objective is to assess and compare changes in body weight among the said patients. Methodology This observational prospective study was carried out at the Department of Orthodontics and Dentofacial Orthopaedics, Sardar Begum Dental College, Peshawar. Every patient gave their informed consent and expressed their willingness for participation in the study. An online sample size calculator (OpenEpi)was used. The confidence level was set to95% with a margin of error of ±5%, and the sample sizecalculated was 150. The patients were chosen using a convenient sampling technique. The patients' weights were recorded on the days of bonding (T0) and one month (T1), two months (T2), and three months (T3) following bonding. Data was processed using IBM SPSS Statistics for Windows, Version 22.0 (Released 2013; IBM Corp., Armonk, New York, United States). Since the data was not normally distributed, the Wilcoxon signed-rank test was applied. Results The Wilcoxon signed-rank test revealed that body weight was significantly reduced one month, two months, and three months after bonding. The body weight on the day of bonding (median=48.200; n=150) was compared to the body weight after one month (median=47.800; n=150), resulting in a statistically significant decrease (z=-9.480; p=0.000) with a large effect size (r=-0.547). The analysis shows a significant decrease in body weight after one month of orthodontic bonding, with a large effect size indicating a substantial impact of the orthodontic treatment on body weight. Conclusion Fixed orthodontic treatment appears to have an impact on body weight, i.e., weight is reduced after the first, second, and third months of bonding.

Why Does the Gender Gap Exist in China's Labor Market after the Economic Transition?

After China's economic reform, the gender gap in China's labor market has emerged and gradually widened, and this issue has also attracted the attention of the Chinese government. This paper introduces the existing gender differences in China's labor market in terms of labor participation rate, occupational attainment, and income. Employing a combination of literature analysis and factor analysis, this study scrutinizes the primary factors contributing to gender disparities, which include but are not limited to human capital theory, gender segregation, gender discrimination, marital dynamics, and gender role expectations. Finally, the essay proposes some solutions for individuals, enterprises, society, and the government to narrow the gender gap in China's labor market to promote the development and progress of society.

Формирование новых художественных приемов в оформлении спектаклей русской антрепризы на рубеже XIX-XX веков на примере театральных эскизов А. Бенуа, Л. Бакста, Н. Рериха и К. Коровина из фондов Государственного Русского музея

The purpose of the study is to identify the author’s artistic features and the processes of formation of new visual concepts in the design of performances of the Russian theatrical concern at the turn of the 19th-20th centuries in the set design works of A. Benois, L. Bakst, N. Roerich and K. Korovin, based on the analysis of rare sketches from the State Russian Museum. The article examines the key aesthetic and artistic trends that became decisive in the design of theatrical scenery during this period, as well as analyzes the influence of social, cultural and historical factors on the development of stage design. The issues such as the interaction of various art schools, the influence of European trends on Russian theatrical scenography and the development of stylistic trends in the design of performances are also considered. The research is based on the study of sketches that had not previously been shown to the general public and were not included in scientific publications, which characterizes them as a unique artistic material. The scientific novelty of the research lies in the systematization and analysis of sketches in a closed collection to identify innovative visual solutions in the context of socio-cultural changes and public attitudes of the specified period. The result of the study suggests the identification of individual artistic features in the theatrical work of each of the declared masters, which will allow us to talk about the Russian theater as a complex multifaceted system in which visual concepts are an integral part of an individual artistic solution.

Virtual reality modulating dynamics of neuroplasticity: Innovations in neuro-motor rehabilitation.

Virtual reality (VR) technology has emerged as a ground-breaking tool in neuroscience, revolutionizing our understanding of neuroplasticity and its implications for neurological rehabilitation. By immersing individuals in simulated environments, VR induces profound neurobiological transformations, affecting neuronal connectivity, sensory feedback mechanisms, motor learning processes, and cognitive functions. These changes highlight the dynamic interplay between molecular events, synaptic adaptations, and neural reorganization, emphasizing the potential of VR as a therapeutic intervention in various neurological disorders. This comprehensive review delves into the therapeutic applications of VR, focusing on its role in addressing multiple conditions such as stroke, traumatic brain injuries, phobias, and post-traumatic stress disorder. It highlights how VR can enhance motor recovery, cognitive rehabilitation, and emotional resilience, showcasing its potential as an innovative and effective tool in neurological rehabilitation. Integrating molecular neuroscience with VR technology allows for a deeper understanding of the molecular mechanisms underlying neuroplasticity, opening doors to personalized interventions and precise treatment strategies for individuals with neurological impairments. Moreover, the review emphasizes the ethical considerations and challenges that come with implementing VR-based interventions in clinical practice, stressing the importance of data privacy, informed consent, and collaborative interdisciplinary efforts. By leveraging advanced molecular imaging techniques, VR-based research methodologies, and computational modelling, the review envisions a future where VR technology plays a central role in revolutionizing neuroscience research and clinical neurorehabilitation, ultimately providing tailored and impactful solutions for individuals facing neurological challenges.

Aplikasi Pembelajaran Tajwid dan Nagham Al-Qur’an Berbasis Web

Mastery of Tajweed and Nagham Al-Quran is crucial for accurate and melodious Quranic recitation. However, access to qualified teachers or reputable learning institutions is not always readily available to everyone. To address this challenge, this research aims to develop a web-based application that facilitates independent and interactive learning of Tajweed and Nagham Al-Quran. The study employs a structured software development methodology following the Waterfall model. Data collection involved literature reviews and interviews with subject matter experts on Tajweed rules, which served as the foundational basis for developing the application’s content. Key features of the application include modules for learning Tajweed and Nagham, Quranic reading exercises, a Tajweed glossary explaining key terminologies, and audio-visual resources to demonstrate clear examples of recitation.Testing results confirm that the application effectively aids users in comprehending and applying Tajweed and Nagham principles. Functional testing verified that all features operate as intended, highlighting the application’s reliability as a self-directed learning tool. This research concludes that the web-based Tajweed and Nagham Al-Quran learning application provides users with a more accessible and flexible platform to enhance their Quranic recitation skills. Future development is recommended to include a mobile version of the application to improve accessibility. Additional interactive features, such as discussion forums for experience sharing and advanced automated recitation evaluation tools, are also proposed to enhance the learning experience. The application leverages web-based technology and adheres to the Waterfall model to ensure a systematic development process. Its web-based nature enables compatibility across a variety of internet-connected devices, significantly broadening user reach. In summary, this research demonstrates that a Tajweed and Nagham Al-Quran learning application bridges the gap in access to quality education, offering an innovative and effective solution for individuals seeking to deepen their Quranic knowledge and recitation skills.

Fuzzy Self-tuning Bees Algorithm for designing optimal product lines

The Product Line Design (PLD) problem is an NP-hard combinatorial optimization problem in marketing that aims at determining an optimal product line through which a firm can optimize a desired objective, like its profits or market share. Since the PLD problem has been proved to have high complexity in real-life applications, high-quality solutions have been detected by researchers who develop various optimization methods and test their performance. The Bees Algorithm (BA) is a successful swarm intelligent optimization algorithm which is based on the behavior of bees. The aim of this research is to develop and assess BA in the optimal PLD problem. In this effort, a set of fuzzy rules has been developed to autonomously compute parameters for each individual solution throughout the optimization process. The performance of two BA variants is compared with those of popular previous approaches, using both real and simulated data of customer preferences. The findings reveal that BA constitutes an enhanced alternative approach for designing optimal product lines.