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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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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 Keywords

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

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 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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 Keywords

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

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

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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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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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A MACHINE LEARNING METHODOLOGY FOR DIAGNOSING CHRONIC KIDNEY DISEASE

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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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 Keywords

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

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

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

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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.

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 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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This project introduces an innovative solution for automating attendance management through facial recognition technology. By harnessing the power of image processing and machine learning algorithms, our system offers a seamless and efficient way to track attendance in various educational and organizational settings. The user-friendly interface allows for easy registration of students or employees, capturing their facial data for identification during attendance sessions. With real-time recognition capabilities, the system accurately records attendance without manual intervention, saving time and resources for administrators. Additionally, comprehensive reporting features enable detailed analysis of attendance trends, contributing to better decision-making and improved operational efficiency. Overall, our automated attendance system revolutionizes traditional methods, offering a reliable and convenient solution for attendance management needs.

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Automation, Attendance Management, Realtime, Analysis

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This research paper explores the landscape of adversarial attacks and defenses in generative artificial intelligence (AI) systems. It provides an overview of different attack techniques and their impact on AI models. The paper also discusses various defense mechanisms, including adversarial training, randomization techniques, and model monitoring. Evaluating the effectiveness of attacks and defenses is discussed, along with the need for ongoing research to stay ahead of evolving threats. The research aims to enhance understanding of adversarial threats in generative AI and promote the development of secure deployment strategies for these technologies.

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Artificial Intelligence, Adversarial, Generative, Gradient, Perturbation, Deep Learning

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The study paper, "MalwareMind: AI-Enhanced Security Solutions," presents the integration of artificial intelligence into cybersecurity systems to counter the increasing complexity of malware threats. The text examines a wide range of AI methods, including Exploratory Data Analysis, Support Vector Machines, Decision Trees, Random Forest, and K-Nearest Neighbors and their respective kernelized models. This demonstrates the critical importance of these methods in improving malware detection and protection. Through a series of case studies, the research paper demonstrates the increased accuracy and versatility of AI-powered algorithms in identifying various types of malware. This signals a phenomenon where more intelligent and responsive security systems are utilized. Due to the evolving nature of cybersecurity threats, "MalwareMind" presents the potential of artificial intelligence to undermine the modern approach to cybersecurity. It has viable approaches to the creation of complex and strong security frameworks.

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 Keywords

Cybersecurity, Exploratory Data Analysis (EDA), Machine Learning, Random Forest, K-Nearest Neighbors (KNN), Kernel Methods, Support Vector Machines (SVM), Malware Detection, Artificial Intelligence (AI).

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GRE Quiz platform transcends conventional learning methods by offering a dynamic and immersive quiz experience. This dynamic tool is meticulously crafted to cater to the unique needs of GRE aspirants, offering a comprehensive array of features aimed at enhancing their understanding and performance across Verbal Reasoning, Quantitative Reasoning. The primary objective of a quiz application is to provide GRE sample papers and practice tests on a regular basis to evaluate their test performance and gain familiarity with the test pattern. Dive into questions mirroring the exam format. This helps in knowing weaker and stronger areas. The three-level quizzes--easy, medium, and hard--offer consumers a personalized experience by letting them choose tests that correspond with their learning objectives.

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User Friendly GRE quiz Platform , Three levels: Easy , Medium , Hard

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Inclusive education entails that all children have access to high-quality education in a welcoming and inclusive setting, regardless of their background, abilities, or impairments. It is giving every child, including those with special needs, equal chances for participation and learning in regular schools within the community. The goals of inclusive education are to break down obstacles, enhance outcomes, and prevent discrimination while fostering diversity, acceptance, and support for all students. It discusses the challenges and barriers hindering the effective implementation of inclusive education practices, This includes issues such as peer pressure, lack of teacher training and adaptability, poor infrastructure, limited parental and community involvement, insufficient collaboration among educational agencies, rejection, geographic disparities, financial constraints, large class sizes, and teacher attitudes and accountability issues. The paper highlights the significance of addressing attitudinal and social barriers to ensure education for all students. It also stresses the necessity for continuous training and assistance for teachers and students to promote a culture of diversity and inclusion in educational settings.

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Inclusive Education, Diversity, Special Needs, Classroom Inclusion, and Educational Barriers

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 Abstract

Background- The SCREEN whether its computer, tablet, laptop, (video games) mobile, and television is a representation of our modern world. The study was conducted to examine the influence of television and mobile as babysitter and it's physiological and psychological hazards on Preschoolers, age 3-5 years. Material and Method- A convenience sample of total 30 parents having children between the age group of 3 to 5 years were included. A semi-structured interview with a combination of open and close ended questions was conducted. The thematic analysis was done for analyzing the content of the interviews. Result and Conclusion- It was concluded from the results that the greatest factor of mobile and television overuse by these preschoolers is the result of ignorant parental attitude. The consequences of using mobile and watching television are that screens might obstruct child's interaction with others and physical exploration of the world--both of which play a role in typical development during early childhood. Research is focusing more on watching television and mobile devices use, and findings indicate that the number of devices, the content, and the duration all play a significant role in determining the impacts of screen time. It was found that laziness/ lethargy is the psychological consequence which had been seen in most children and the physiological consequences that had been found in most of children was difficulty in concentration.

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babysitter, physiological and psychological hazards, preschoolers, convenience sample, open-ended interview, parental benefit, parent's attitude

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The blend of digital technology into judicial system has brought significant development in the administration of justice, reforming the traditional system of judiciary, and expanding the new era of rule of law. The impact of digitalization is examined in the judicial review. Through a comprehensive analysis of scholarly literature, case studies and empirical data; the research paper aims into the multifaceted dimensions of digitalization within the realm of judicial review. Further the research paper examines the opportunities and challenges presented by digitalization for the rule of law, including issues of access to justice, privacy concerns and potential algorithmic bias. It also examines the implication of digitalisation for judicial independence, accountability, and the legitimacy of legal institutions. By critically evaluating the effects of digitalization on judicial review, research paper contributes to deeper understanding of the transformative potential of technology in shaping the future of the rule of the law. The concept of the rule of law has been reshaped from time to time with the need of the society. In the current digital era, it is evolving at an alarming rate. Before we understand the concept and upcoming threats of digitalization of e-judiciary it is becoming the essential part of judicial system. Hence an attempt to analyse the new horizons of the rule of the law in the digitalization and judicial review.

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Digital Technology, Judicial System, Administration of Justice, Traditional Judiciary, Rule of Law, Judicial Review, and Legal Institutions.

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Political activities can affect human life in both positive and negative ways. The aim of this research is to critically analyse the role newspaper in changing people opinions about political issues. Various questions that are raised during the research are mentioned. The impact of newspapers on politics and their influence on the public is highlighted. Through the inclusion of the multi-step flow theory, the influence of newspapers and politics on citizens has been analyzed. Various questions that are raised during the research are mentioned. Through this research, public opinions about the newspaper and political issues are gathered by following primary quantitative method. All the data are further analyzed by using SPSS method. The significant value obtained by analysing the gathered information through SPSS is 0.007 which indicates a reliable outcome. For overcoming the issue of false news, government entities restrict illegal behaviour in journalism sectors and news industries involve A.I. based technologies to determine the fake information in the pool of information.

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 Keywords

Transparency, government entities, public opinions, media, politics, newspaper

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 Abstract

Evaluating subjective answers presents significant challenges for teachers. They must meticulously examine each word to assign scores,considering factors such as their own mental state, fatigue, and objectivity.While machines can easily assess Subjective answers by matching them with concise responses provided by students.Addressing these Challenges of assessment ,this system is being proposed.A previous proposed system for subjective answer evaluation using ML incorporates advanced NLP techniques. The system addresses challenges like handling ambiguity, domain adaptation, and bias mitigation offering an automated approach for fair subjective answer assessments.The proposed system have different modules like Preprocessing Module,Evaluation Module,Result Predicting Module and Similarity Measurement Module.The answers will be checked on basis of the Answer length, Keyword Check, Grammar check,Context and Semantic Similarity and Cosine Similarity against model answer. The software will take copy of the answer as an input and then after the preprocessing step it will extract the test of the answer. This text will again go through processing to build a model of keywords and feature sets. Model answer sets and keywords categorized as mentioned will be the input as well. Classifier will then, based on the training will give marks to the answers will be the final output

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 Keywords

NLP, Contextual similarities, Semantic Analysis,ML,WordNet,Cosine similarity

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Sheetpitta, Udard, Kotha are types of skin disorders.These can be correlated with urticaria in modern science Sheetpitta, Udard, Kotha are having similar symptoms and causative factors,Sheetpitta is one of the disorders caused by virudhahara sevan. Sheetapitta in which vata, kapha are two doshas which causes due to doshaprakopaaka hetu & combination with pitta dosha &creat the symptoms like Varti Damshta Samsthana Shotha(Inflammation like an insect bite), Kandu Bahula(Sever itching), Toda Bahula( excessive pain like pricking), Chardi( vomiting), Jvara( fever), Vidaha(Burning Sensation).etc. There are internal & exertal Ayurvedic medicines which are used in treating these disorders.

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Sheetpitta, Udarda, Kotha, Ayurvedic management

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