Volume 12 | Issue 4 |

 Abstract

The rise of online education has brought about a new era of learning, bringing with it various opportunities and challenges for both educators and learners. One particular challenge is the lack of comprehensive, user-friendly platforms that cater to the changing needs of modern education. To address this issue, the focus of this study is on developing an online class and module web application using the MERN (MongoDB, Express.js, React.js, Node.js) stack. Acknowledging the necessity for an all-encompassing solution, we explore the intricacies of educational technology, pinpointing the main drawbacks of current platforms. Our strategy revolves around creating a well-rounded application that is carefully designed to tackle these shortcomings and provide a compelling option for educators and learners. By following a thorough process that includes conceptualization, design, implementation, and evaluation, we navigate the complex world of web application development, guided by the principles of usability, scalability, and adaptability. Leveraging the flexibility of the MERN stack, our platform aims to deliver a smooth user experience that encourages collaboration, engagement, and knowledge retention. Features such as real-time communication, user-friendly interfaces, and robust module management capabilities come together to establish a dynamic learning environment that caters to a variety of educational settings.

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 Keywords

Online Class, Web Application, MERN, Modules

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Online collaboration technologies have been more widely used in higher education in recent years, opening up new channels for participation, communication, and interaction between students and teachers. The purpose of this research article is to examine the variables that affect higher education settings' use of online collaboration technologies. This study examines the advantages, difficulties, and ramifications of incorporating such technologies into academic settings through a thorough analysis of the body of research and empirical data. It also looks at the pedagogical techniques used, the technology setup needed, and the effects on student learning outcomes. Educators and institutions may improve teaching and learning practices in higher education by making well-informed decisions by being aware of the challenges associated with the implementation of online collaboration technologies. Findings reveal diverse perceptions among educators and students regarding the ease of use, pedagogical value, effectiveness in facilitating learning, communication and interaction, technical challenges, and privacy and security concerns of online collaboration tools. Adoption-promoting factors include innovations in technology, ease of use and flexibility, improved communication, pedagogical integration, and institutional support; adoption-hindering factors include resistance to change, the digital divide, technological difficulties, pedagogical misalignment, and privacy issues. In conclusion, this research provides valuable insights into the adoption of online collaboration tools in higher education and highlights the importance of addressing various factors to promote their effective use in teaching and learning practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Online Collaboration Tools, Higher Education, Adoption, Qualitative Research.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Frothy Disturbance Intrusion Detection Systems (FIDSs) can help detect and prevent security attacks using the Support Vector Machine (SVM) algorithm. Recognizing the importance of FIDS in protecting various domains linked to the internet, we concentrate on adapting traditional intrusion detection methods for the landscape, which faces challenges such as resource constraints and limited memory and battery capacity. Our study entails the creation of a lightweight attack detection technique that uses a supervised machine learning-based FIDS using the SVM algorithm. We use simulations to demonstrate the usefulness of the proposed SVM-based FIDS classifier, which uses a combination of two or three complex features and achieves satisfactory classification accuracy and detection time. This strategy has the ability to enhance application security by effectively addressing the particular.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Edge Computing, Frothy Disturbance, Distributed Systems, FIDs, SVM.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The main task of financial management is to choose the best results from the investment, and this is the most important decision for the finance manager because any decision made by the president in this regard can affect the operations of the company and its profits for many years. The purpose of the research is to examine the decisions of companies in the financial investment process by examining the importance of capital investment in organizations and to determine the sources of financial capital in which the company will be well invested in various ways. business decisions. It also provides information on cash flow and cash flow for each year. Thus, the comparison gives a clear idea of the investment and return that will be useful for the next year. The analysis is based on data collected from the Income and Expense Report and the Business Report. Financial resources such as present value, rate of return, and method of repayment over time are used in the analysis of the collected data. Some other estimation tools such as standard deviation, correlation analysis, and analysis of variance were also used in this study.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Profit, Investment, Capitalisation, Capital planning, Capital budgeting, Finance management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Criminal activity is consistently one of the most significant societal issues, endangering both individuals and public safety. The government, police, and public may all implement efficient crime prevention strategies with the aid of accurate crime prediction. This study reviews the literature on crime prediction methodically from several temporal and spatial angles. With an emphasis on prediction techniques, we provide an overview of the state of crime prediction research as of right now from four angles: prediction content, crime kinds, methodology, and assessment. Crime prediction on different temporal and spatial scales may be broken down into three categories: micro-, meso-, and macro-level prediction for spatial crime, and short-, medium-, and long-term prediction for temporal crime prediction. A range of assessment criteria and crime prediction techniques are also compiled, and various models and prediction techniques are contrasted and assessed. After reviewing the literature, it was discovered that there are still a lot of gaps in the knowledge base. These gaps include: (i) the difficulty of effectively handling data sparsity; (ii) the lack of predictive model practicality, interpretability, and transparency; (iii) the evaluation system's relative simplicity; and (iv) the paucity of research on the application of decision-making. To address the issues mentioned above, the following recommendations are made in this regard: In order to deal with sparse data, (i) transformer learning technology is used; (ii) model interpretation techniques, such as Shapley additive explanations (SHAPs), are introduced; (iii) a set of standard evaluation systems for crime prediction at various scales is established in order to standardise data use and evaluation metrics; and (iv) reinforcement learning is integrated in order to achieve more accurate prediction while encouraging the transformation of the application results.

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 Keywords

Crime; Public Security; Multi-Scale; Spatial-Temporal; Crime Prediction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The aim of this paper is to automate the Chronic Kidney Disease(CKD) detection process using Lab Test and Computed Tomography(CT) scan images by employing a Deep Learning model using small sample size.Because basic diagnosis is not a straight forward process and needs algorithm based detections. In this paper, we use Bidirectional Long Short-Term Memory and Deep Convolution Neural Network algorithms based on Artificial intelligence(AI) to extract and evaluate the features of different classes using pre-processed CKD datasets to evaluate the CKD early stages. The CT images are classified into four classes Normal, Tumor, Cyst and Stone. In this paper we also find out the precision loss and model loss. The model outperforms traditional data method classification technique by providing much better predictive ability. This small initiative will detect the CKD in early stages by using Deep Learning methods effectively, as it is a work related to medical field for helping the society and saving the human life.

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 Keywords

A MACHINE LEARNING METHODOLOGY FOR DIAGNOSING CHRONIC KIDNEY DISEASE

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 Abstract

This abstract examines the intricate relationship between gender and crime, exploring the multifaceted dynamics that shape criminal behavior and justice outcomes. Drawing on interdisciplinary research from sociology, criminology, psychology, and law, this paper delves into the various factors influencing gender disparities in criminal involvement, victimization, and processing within the criminal justice system. It discusses biological, social, and cultural influences on gendered patterns of crime, as well as the impact of societal attitudes, stereotypes, and institutional responses. Moreover, the abstract highlights the implications of gendered perspectives for crime prevention strategies, policy development, and the pursuit of gender-responsive justice. By synthesizing empirical evidence and theoretical frameworks, this abstract contributes to a deeper understanding of the intersectionality between gender and crime, shedding light on key issues and avenues for future research and policy interventions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

LAW, RIGHTS , JUSTICE , CRPC, GENDER AND CRIME, HUMAN RIGHTS,ABUSE OF POWER

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Creative Commons Attribution 4.0 and The Open Definition

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Electrical signals in the heart are recorded by the electrocardiogram (ECG). The electrocardiogram is a diagnostic tool for heart diseases such as heart arrhythmias, sleep apnea, and myocardial infarction. To prioritize the detection of abnormalities in ECG data, this research presents a technology identified using machine learning algorithms. The final model that emerges is a method defined by regular heart rate analysis. The model, which is expected to reduce power consumption by more than 50%, is used in ARM Cortex M4-based embedded devices. The system is well-prepared for clinical use due to its real-time adaptation, single processing, reduced complexity and interpretation. It is an Internet of Things (IoT)-enabled wearable edge sensor that provides the necessary sensitivity and accuracy by consuming very little power.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Electrical signals; ARM Cortex M4; real-time flexibility; Single functionality; Internet of Things; Irregular heartbeat.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Waste management is becoming a major issue for communities worldwide. Burning agricultural waste is one of the major causes of pollution in India. Burning agricultural waste is one of the major causes of pollution in India. Thus, to meet the ever-increasing demand of building materials, new sustainable materials are needed. The potential application of agro-waste', like Rice stems, wheat steam, as the ingredient for alternative sustainable construction materials in the form of bricks. In this paper study of compressive strength and crushing strength weight of bricks is done by adding rice stems into the bricks of size 230mm*110mm*70mm* were manufactured .The bricks samples were tested at 7 , 14 and21 days. This demonstrates that bricks containing rice stem to exhibits compressive strength 54% of regular fly ash bricks, Weight of bio bricks is lesser than fly ash bricks ,water absorption is also more than fly ash bricks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fly ash, wheat stem, Bio bricks, Compressive strength.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Generally, large retail shops struggle to track their customers purchasing heavy merchandise. These sizable transactions often involve special offers that allow customers to pay in installments over a set period. These sizable transactions often involve special offers that allow customers to pay in installments over a set period. To address this issue, the Deferred Instalment and Automatic Payment Monitoring (DIAFM) system steps in to assist retailers. This system includes a feature that facilitates periodic notifications and tracking. One innovative aspect of this system is its automatic bill payment reminder functionality. This method streamlines the process by automatically handling the payment of regular bills for users at predefined intervals. The method also includes providing an automatic reminder process, where the automatic reminder process automatically sends reminders before the automatic payment of the bills at predetermined time intervals, enhancing accountability and reducing financial risks for both customers and retailers.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Customers, Heavy merchandise, Special offers, installments, Deferred Instalment and Automatic Payment Monitoring (DIAFM) system, Periodic notifications, Tracking, Automatic bill payment reminder functionality, Streamlines, Regular bills, Predefined intervals, Automatic reminder process, Accountability, Financial risks, Retailers

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Creative Commons Attribution 4.0 and The Open Definition