Volume 13 | Issue 5 |

 Abstract

??????? ???? ????? ?? ?????? ?? ????? ?? ??????? ???? ???? ????? ?? ??? ?? ?????? ?? ??????????? ???? ???? ????? ????? ??? ??????? ?? ??????????????? ?? ?? ???? ???? ?????? ???? ?? ??????????????? ?? ?????? ?? ???? ?? - ???? ???? ????????? ????????????????? ?????????? ????????? ?????????? ??????????? ????????? ??????????? ? ???? ??????????? ?????????? ? ??????? ??? ???? ? ??????? ???????????? ??????????? ???? ??????? ???? ? ???????? ?????? ?????? ? ???????? ????????????? ???????? ???? ??????? ??? ?? '??????????????' ??? '???????????' ?? ???????? ??????????????? ?? ?????? ??? ????? ???? ??? ??? ??????????????? ?? ?????? ??? ???? ??????? ???????? ?? ?????? ?? ?? ?? ?? ??????? ???????? ??? ?? ??????? ?? ?? ??? ???? ??? ???????? ?????? ???? ?? ??? ?????? ?? ??? ????????? ??????? ????? ?????????? ?? ?? ????? ?? ???? ?? ???, ???? ?????????? ?? ???? ?????, ?????????? ? ????????? ?? ???? ?????? ??? ?? ???????, ?????? ?? ???????? ??? ????? ?? ?? ??????? ?? ????? ????? ?? ??????? ??????? ?? ????? ?? ???????? ?? ?????? ????? ???????????????, ???????????, ????????? ???? ???? ?? ??? ?????? ? ????????? ??, ?? ??? ???????? ?? ?? ??????? ?? ?????? ?? ????? ?????????? ?? ?? ?????? ???? ?? ??????? ?? ??? ??????????????? ????? ??? ?? ???? ??????? ??? ?? ??????? ?? ?????????????? ? ????????? ???? ?????? ?? ?????? ???? ?? ???? ?? ???? ?? ????????? ???? ??? ???? ??????? ???? ?? ??? ???? ???? ?? ?????? ?? ????? ?????? ?? ?????? ??????? ?? ??? ?? ???? ??? ??????????? ?????? ?? ?????? '???? ???? ???? ???? ?? ????? ???? ??????? ?? ??????? ???? ??, ???? ?? ???? ???? ???? ?? ???? ????????? ???? ?? ?????? ????? ?????? ?? ?????? ???? ??? ???? ????-???????? ?? ?? ????? ?? ???? ???? ?? ??? ???? ????? ?? ??? ?????? ?? ???? ???? ??????? ???? ???? ?????? ???? ???'? ??? ?? ??????????????? ?? ???? ???? ?? ???? ?????? ??? ????

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

????????????, ??????????, ??????, ????????, ?????? ???????

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Image Captioning and Similarity Generator, aims to develop a Python-based platform that merges image captioning with similarity detection to improve multimedia search and management processes. The system harnesses deep learning methods, specifically Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) with attention mechanisms for producing captions that resemble human-generated descriptions. The captioning model is trained using extensive datasets like Flicker8k, enabling it to recognize complex visual patterns and their corresponding textual descriptions. Beyond generating captions, the system also incorporates a similarity detection component, which compares images based on visual characteristics and semantic embeddings. By leveraging pre-trained CNN models, this component generates image embeddings and uses similarity metrics to identify and rank images that are visually or conceptually related to the input image.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image similarity and caption Generator ,Deep Learning,Convolutional Neural Network, Similarity Detection, Multimedia Search

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Small and medium-sized companies (SME), especially in development areas, continue to meet important challenges in using digital businesses for business operations. Most commercially available billing and inventory management systems are either membership-based, cloud-agent or a level of technical expertise requires a level of technical expertise that lacks many business owners. These obstacles contribute to journal maps, delayed invoices and inability to monitor stock levels or outstanding payments. The concept recognized this difference and was conceptualized and developed as a mobile-first solution for the purpose of making the daily business administration more accessible, reliable and expensive for local enterprises.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Android Application, Business application, DigiVyapaar

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Creative Commons Attribution 4.0 and The Open Definition

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Integrating educational technology remains a crucial concern in elementary education. This research explored how engagement in professional development mediated the relationship between school innovation climate and educational technology usage. Using a non-experimental, descriptive-correlational design with path analysis, data were collected from 100 public and private elementary school teachers in Davao City, Philippines. The results revealed that engagement significantly mediates the relationship between the predictor and outcome variables. These findings are supported by Self-Determination Theory (Deci & Ryan, 1985), which emphasizes that autonomy-supportive environments foster intrinsic motivation and engagement. The study highlights how an innovative school climate can enhance teachers' motivation to participate in professional development, ultimately leading to greater technological integration. Future research may explore other motivational or contextual factors influencing technology use. Additionally, school leaders and policymakers are encouraged to cultivate innovation and promote sustained teacher development to support effective educational technology practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

School innovation climate, professional development engagement, educational technology, and mediation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Farming is increasingly benefiting from machine learning (ML). It makes it simpler for farmers to safeguard their crops and increase yields by assisting them in anticipating and managing crop illnesses. As agricultural systems face increasing challenges due to population growth and the need for sustainable farming practices, ML offers data-driven solutions for improving productivity and reducing environmental impacts This study examines the application of machine learning (ML) to crop disease diagnosis, emphasizing hybrid models that improve accuracy by utilizing deep learning architectures such as VGG-16 and ResNet. Despite the advancements, challenges such as class imbalance, overfitting, and underfitting still pose limitations, especially when applied to diverse datasets. Methods like data augmentation and the incorporation of Internet of Things (IoT) devices have been suggested as solutions to these problems. This research highlights the potential of ML to revolutionize agricultural practices, offering precise and effective tools for crop disease management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Naive Bayes, k-means clustering, training, classification, random forests, and artificial neural networks

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Anisocoria, characterized by unequal pupil sizes, can be an early indicator of underlying neurological or ophthalmological conditions. This project presents a real-time, non-invasive anisocoria detection system using computer vision and facial landmark analysis. The system utilizes a standard webcam to capture video input and employs Media Pipe's face mesh for precise localization of the eye region. Pupil detection is performed using the Hough Circle Transform to measure the radius of each pupil. The difference in pupil size is compared against a defined threshold to determine the presence of anisocoria. A user-friendly GUI built with Tkinter allows users to initiate and control the detection process, displaying the diagnostic result in real time. Additionally, each session is logged in a local SQLite database with the patient's name, detection result, and timestamp to support accurate medical record-keeping. To improve usability, the system includes audio alerts upon detection and performs continuous monitoring for consistent observation. This system offers a low-cost, accessible tool for early detection of anisocoria, making it suitable for clinical screenings, remote diagnosis, and telemedicine applications. It is designed to operate effectively under varying lighting conditions and is extensible to incorporate advanced features such as frame capture on detection, CSV-based logging, and live graphical visualization of pupil sizes. Future improvements may include integration with electronic health records (EHR) systems and support for video file analysis, making it a versatile solution for modern healthcare environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Anisocoria Detection, Computer vision, Facial Landmark Analysis, Media Pipe, Face Mesh, Hough Circle Transform, Tkinter GUI, Webcam-Based Monitoring, Real-Time Diagnosis, Remote Health Monitoring, Medical Image Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper introduces a unique furniture shopping application that combines Augmented Reality (AR) and Artificial Incorporating. AI-driven solutions to refine the overall user experience. sApp enables users to place virtual furniture in their real room environments using AR, helping them check how well the furniture fits and matches with their interior design. AI-based suggestions guide users in choosing furniture that aligns with their style, room aesthetics, and current design trends. The app uses advanced algorithms for smooth real-time AR visualization and smart data analysis to ensure a user-friendly interface. Early testing results show a boost in customer satisfaction and a noticeable drop in return rates. This research also discusses the technical challenges faced, insights from user feedback, and potential areas for future development.

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Android Application, Kotlin, Augmented Reality (AR), Artificial Intelligence (AI)

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid advancement in artificial intelligence (AI) and machine learning (ML) has revolutionized the healthcare industry by enhancing patient care, enabling real-time diagnostics, and transforming traditional health monitoring practices. In this paper, we present the design and implementation of a multimodal biometric health assessment system integrated within an AI-powered Healthcare Patient Portal Dashboard. This system combines user interaction, biometric data analysis, machine learning-based disease prediction, and an administrative panel for healthcare providers. The backend is developed using Python Flask, while the frontend is implemented using HTML and JavaScript to provide a responsive and user-friendly experience. The platform supports disease prediction in the cardiology and neurology domains by analyzing symptoms alongside biometric inputs such as blood pressure, glucose levels, and complete blood count (CBC) data. Through the integration of AI models trained on medical datasets, the system enhances early detection, promotes patient engagement, and supports clinicians in decision-making. The comprehensive design empowers users with insights and contributes to a

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BIOMETRIC MEDICAL COMPUTER APP WEBSITE

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Creative Commons Attribution 4.0 and The Open Definition

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A budget is the monetary and quantitative expressions of business plans and policies to be pursued in the future period of time the term budgeting is used for preparing budgets and other procedures for planning co-ordination and control of business enterprise. Budgetary control is the process of determining various budgeted figures for the enterprises for the future period and then comparing the budgeted figures with the actual performance for calculating variations, if any first of all budgets are prepared and then actual results are recorded.

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Creative Commons Attribution 4.0 and The Open Definition

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Variance analysis, performance evaluation , control measures

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Creative Commons Attribution 4.0 and The Open Definition

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This essay investigates the societal influence of Artificial Intelligence (AI) on Supply Chain Management (SCM). It analyzes how AI-driven tools like machine learning, predictive analytics, and automation are revolutionizing supply chains. AI enhances demand forecasting, automates processes, and increases visibility, leading to more effective, cost-efficient, and transparent supply chain operations. The study evaluates the applications, challenges, and future potential of AI in enhancing SCM. By addressing these aspects, this paper contributes to understanding AI's crucial role in modernizing and optimizing supply chain practices.

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Creative Commons Attribution 4.0 and The Open Definition

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Intelligent Systems, Computational Learning, Forecasting Analytics, Supply Chain Optimization, Process Automation, Operational Transparency, Efficiency Enhancement, Uncertainty Mitigation.

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Creative Commons Attribution 4.0 and The Open Definition