Volume 13 | Issue 4

 Abstract

Diabetes are widespread health issues around the world. Both Insulin Dependent (Type 1) Diabetes Mellitus and (Type 2) Diabetes Mellitus are prevalent, with Type 2 diabetes being more common and often associated with lifestyle and diet. The prevalence of heart disease varies by region, but it affects people of all ages, genders, and backgrounds. Neural networks techniques have been applied to diabetes in various ways to assist in diagnosis, management, and treatment. Neural networks can be used to develop predictive models that analyses patient data, including Cholesterol, BMI, to diagnose diabetes or predict the risk of developing diabetes in the future. Neural networks can be trained to predict the risk of diabetes. The existing dataset is pre-processed. Using feature extractions, highly correlated features are extracted. The performance of the model was evaluated through accuracy. A user interface was also developed for clinical use.

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 Keywords

Random Forest, BPNN, PCA, CV, ECV, Cardiovascular, Diabetic,PSO

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Phishing attacks are one of the greatest threats to online security, where fraud websites deceive users into giving out sensitive information. Traditional methods of detection, such as blacklists and heuristic-based systems, often fail in identifying newly created or sophisticated phishing websites. This study proposes an intelligent phishing website detection system using Convolutional Neural Networks (CNNs) in analyzing URLs and associated features. Using labeled URLs, the system employs such attributes such as URL length, domain age, HTTPS usage, and redirection patterns to train a CNN model.The potential applications include browser extensions, email filters, and cybersecurity tools. The proposed approach improves user protection against phishing attacks and mitigates the risks associated with it. This research underlines the efficiency of machine learning in dealing with dynamic and evolving cybersecurity challenges.

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Phishing Detection, URL Analysis, Machine Learning, Deep Learning, Feature Extraction, Decision Trees, Real-Time Detection, Convolutional Neural Networks (CNNs)

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A dual-axis steering mechanism is an advanced steering system that allows a vehicle or robotic system to operate with two independent axes of rotation, usually one horizontal and one vertical. This innovative mechanism is designed to provide enhanced maneuverability, precision, and control over conventional single-axis steering systems. By enabling movement along both axes simultaneously or independently, the dual-axis steering mechanism significantly improves a vehicle's ability to navigate through tight spaces, sharp turns, or uneven terrains. In traditional steering systems, movement is restricted to a single axis, typically horizontal, limiting the vehicle's ability to change direction efficiently or perform complex maneuvers. The dual-axis system, on the other hand, allows for more versatile movement, including the ability to make sharp turns while maintaining stability, reduce the turning radius, and increase the adaptability of the system in diverse environments. This makes the dual-axis steering particularly valuable for applications in autonomous vehicles, off-road vehicles, and robotic systems where precise control and navigation are essential. The dual-axis mechanism involves intricate mechanical components, such as rotating joints, actuators, and control systems, which work together to achieve independent movement on both axes. While offering significant benefits in performance, such systems also introduce challenges related to mechanical complexity, weight distribution, energy efficiency, and durability. Nonetheless, the advantages in terms of improved maneuverability, stability, and control make the dual-axis steering a promising technology for future applications in various fields, including robotics, autonomous vehicles, and specialized transportation systems. The dual-axis steering mechanism represents a significant advancement in steering technology, offering enhanced maneuverability, precision, and control compared to traditional single-axis systems. By enabling independent movement along both horizontal and vertical axes, it allows vehicles and robotic systems to navigate complex environments with greater ease, making it especially valuable in applications like autonomous vehicles, off-road navigation, and robotics. While the implementation of dual-axis steering presents challenges such as mechanical complexity, energy efficiency, and weight management, the potential benefits in terms of improved stability, reduced turning radius, and increased adaptability outweigh these obstacles. As technology continues to evolve, the dual-axis steering mechanism is poised to play a pivotal role in the future of advanced mobility and autonomous systems, driving innovation and performance across various industries.

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 Keywords

Conventional steering mechanism,constant velocity axle,Design and planning

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 Abstract

The research conducted focused on examining the unique properties of Agave Americana Flower Stem fiber (AAFS), particularly its behavior under quasi-static tensile conditions. A total of 200 AAFS fibers were subjected to tensile tests using a standard gauge length of 40 mm. Tests spanned seven groups with quantities (N) ranging from 30 to 200. The study aimed to understand the fibers' mechanical traits, as tensile resistance and modulus of elasticity, and to see how different test quantities influence these properties. A significant observation was the dispersion of the tensile characteristics of AAFS fibers, a common trait of natural fibers. To understand this, we applied rigorous statistical tools, including the Weibull distribution at a 95% confidence interval and one-way ANOVA. A mathematical model was produced utilizing data from experiments regarding the tensile behavior of AAFS fibers. The ANN provided correlation coefficients (R2) of 0.9897, 0.9971, 0.9993, and 0.9939 for training, validation, testing, and all datasets respectively, which were able to accurately predict the experimental data. The proposed model would be of tremendous assistance to engineers and designers in obtaining green composite materials that are based on natural fibers and thereby more durable. These methods illuminated the patterns in our results, enriching our understanding of AAFS fiber mechanics.

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Agave americana, Artificial neural network, tensile behavior, statistical methods,Weibull, statistic, hazard function, Wireless sensor network.

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 Abstract

AI Resume Analyzer is an intelligent system that leverages Natural Language Processing (NLP) and Machine Learning to evaluate resumes efficiently. It extracts key information, matches qualifications with job descriptions, and provides insights to streamline recruitment, ensuring accurate, unbiased, and faster candidate screening for HR professionals and employers.

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NLP (Natural Language Processing) Resume Parsing, Resume Analysis, Google Gemini AI, Streamlit Application, Skill Gap Detection, Career Recommendation System, OCR Resume Parsing

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Ayushman Bharat, launched by the Government of India in 2018, represents a landmark reform in the country's healthcare landscape. Envisioned as a step toward Universal Health Coverage (UHC), the scheme combines health insurance for secondary and tertiary care with the establishment of comprehensive primary healthcare infrastructure. This article examines the structure, objectives, implementation, achievements, and emerging challenges of Ayushman Bharat, with a focus on its role in transforming India's healthcare delivery system.

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Ayushman Bharat, Universal Health Coverage (UHC), Public-private partnership, Pradhan Mantri Jan Arogya Yojana (PM-JAY), Health and Wellness Centres (HWCs), out-of-pocket Expenditure

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The aim of this study was to evaluate the knowledge gap by assessing the knowledge and attitude regarding pulmonary TB among the rural population of Charar-i- Sharief Budgam Kashmir. This descriptive study aims to assess the level of knowledge and attitude towards pulmonary tuberculosis (TB) among the rural population of Charar-i-Sharief, Budgam, Kashmir. A quantitative research approach was adopted, and a convenient sampling technique was used to select participants. Data were collected using structured questionnaires for assessing knowledge and attitude, with an interview schedule to facilitate data collection. Data collected was organised and analysed according to the objectives of the study using descriptive and inferential statistics.

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Knowledge, Attitude, pulmonary tuberculosis, AFB, DOTS.

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This research focuses on the creation and assessment of CNN-based models to categorize brain tumors into three main groups: pituitary, meningioma, and glioma. The suggested model is trained and validated using an MRI dataset that is openly accessible. To maximize performance, the study investigates different CNN architectures, pre-processing methods, and training approaches. The efficacy of the model is evaluated using key evaluation metrics like accuracy, precision, recall, and F1- score.

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Brain Tumor Classification, Convolutional Neural Networks(CNN), Magnetic Resonance Imaging(MRI), Deep Learning, Medical Image Analysis, Transfer Learning

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 Abstract

Students frequently struggle with academic anxiety, which impairs their capacity to study and perform well. Simultaneously, metacognition--the understanding and control of one's own mental processes--is essential for academic achievement. In order to comprehend how students view and control their anxiety through metacognitive techniques, this qualitative study investigates the connection between academic anxiety and metacognitive skills. Semi-structured interviews with college and high school students were used to gather data, and thematic analysis was used to find important trends. According to research, students who regularly practice metacognition report feeling less anxious and performing better academically. Implications for educational interventions and future research areas are discussed in the study's conclusion.

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academic anxiety, metacognition, qualitative, interventions.

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 Abstract

In the twenty-first century the advancement and rapid development in the field of Information and Communication Technology (ICT) has brought change in use and pedagogy of English language. It has also enabled new forms of discourse as well as knowledge and new ways to create and participate in communities. Freeman and Anderson have pointed out in their book 'Techniques & Principles in Language Teaching' - "Rapid evolution of communication technologies has changed language pedagogy and language use, enabling new forms of discourse, new forms of authorship and new ways to create and participate in communities (Kern 2006:183, cited in Freeman and Anderson 2019: 200)". Internet and Social Media are the part of ICT that have tremendous effect on daily activities of human life. People spend much of their time on internet and social media for various purposes like entertainment, acquiring knowledge, sharing and collecting data and information, and so on. It provides lots of opportunities and creates attractive environment to learn English language easily. At the same time it also provides the liberty to distort the standard format of language use. Internet enables the users share information, ideas, data and services through various platforms of ICT. It connects billions of users across the globe. Social media platforms like Twitter, YouTube, Instagram, Facebook, and so on provide a space to create an interesting and entertaining environment to learn English language efficiently, as English is used frequently on these platforms. Internet and social media facilitate and motivate the learners to learn English language in an unconscious way. English language plays a crucial role in modern life and is also influencing each and every field of human activities like education, trade and commerce, art and culture, research and development, science and technology, administration and jurisdiction and many more. The purpose of this research paper is to explore the role of various internet services like online music, email, webpages as well as some educational websites and social media platforms in gaining competence in the use of English language, in both spoken and written forms. ICT encourages the learners to develop the four language skills, like listening, speaking, reading, and writing and components (pronunciation, vocabulary, phrase, sentence formation and its use in proper context) of English language. It can be practised by language learners through constant use of audio- visual aids, emails, chats, messages, reading a novel in English, visiting e-library, the meaning of emoji or icon and logo. It helps in interacting and communicating with English language experts on internet.

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 Keywords

Communication Skills, ELT, ICT, Internet, Social Media, Technology.

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 Abstract

The rectifier basically converts AC input to the DC output. In general, different types of rectifier topologies are available like diode bridge rectifier, thyristor-controlled rectifier, PWM based rectifier etc. This paper discusses front end PWM rectifier, its working principle and generation of gate pulses using two different modulation techniques namely sine pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM). This paper also features the description of 3-level front end PWM based rectifier and generation of 3-level space vector PWM. Simulation models of two level SPWM, two level SVPWM and three level SVPWM based front end rectifier are developed and simulated in MATLAB / SIMULINK platform and a comparative analysis is presented. sinusoidal Pulse width modulation (SPWM) technique and direct power control (DPC) Pulse width modulation technique for three-phase AC to DC converters using. MATLAB/SIMULINK software. Pulse width modulation (PWM) techniques are frequently used due to improved performance such as unity power factor operation with reduced total harmonic distortion (THD) at ac mains. In this paper simulation models for both PWM techniques are simulated with closed loop at rated load condition and then comparative analysis has been done in terms of input current THD and power factor. DPC is the efficient technique because of the better performance.

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 Keywords

AC/DC converter, total harmonic distortion, SPWM, PI control.

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ABSTRACT Introduction: Brain gym brings movement back into learning. It uses activities and exercises to reduce stress so that the body and mind can work more efficiently in any learning, study, performance or daily life situations. It is a tool which can be integrated into any teaching situation with any age group. A practical, movement based course with the focus being on integrating it into a tool kit of strategies to keep individuals focused and attentive for learning. This brain gym exercises can lead onto further study in the specific use of brain gym movements and educational kinesiology. Objectives of the Study: 1. To assess and compare the pre and post test self esteem and level of concentration among high school students at selected school.2. To assess the effectiveness of training programme on the self esteem and level of concentration among high school students at selected school. 3. To correlate the self esteem and level of concentration among high school students at selected school.4. To associate the self esteem and the level of concentration with their selected demographic variables with the mean differed level among high school students at selected school. Materials and Methods: A pre experimental, one group pre test-post test design was selected with the sample size of 60 high school students by using purposive sampling method with inclusive criteria. The conceptual frame work of Stufflebeam and colleagues was applied. The study was conducted in MASI Metric Higher secondary School. Modified Rosenberg self esteem scale and modified concentration assessment scale was used to assess pretest and post test self esteem and level of concentration. Brain gym exercises done for 15 days duration in 30 minutes. Results: In pre test self esteem score , all sample 60(100%) had moderately adequate self esteem and none of them had adequate self esteem, where as in the post test after administration of brain gym exercises 43(71.67%) had adequate level of self esteem and 17(28.33%) had moderately adequate level of self esteem regarding modified Rosenberg self esteem scale. In pre test 2(3.33%) had inadequate level of concentration and 58(96.67%) had moderately adequate level of concentration. In post test after administration of brain gym exercises on concentration, 40 (66.67%) had adequate level of concentration and 20 (33.33%) had moderately adequate level of concentration towards modified concentration assessment scale. Brain gym exercises found to be effective on the level of self esteem (t=9.178,p=0.001) and level of concentration (t=8.597.p=0.001). Self esteem and concentration found to have a positive correlation (r=0.511) Conclusion: The study pointed out that student's self esteem and concentration can be influenced by brain gym exercises. It will help the learners to have improvement in their academic as well as over all development. The outcome of the study will also provide an awareness to the nursing field.

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KEYWORDS: Brain gym exercise, self-esteem, concentration, school students.

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 Abstract

Digital identity systems are in fact becoming increasingly more common because of efficient service delivery and secure identity verification that are pushed for by governments and also private entities. However, these systems greatly concern certain people since they affect the people's citizenship rights. This paper critically examines digital identity infrastructures' implications for civil, political, and socio-economic rights. Drawing on global case studies and legal frameworks, the paper analyzes how centralized identity systems based on biometrics create new forms of exclusion particularly for marginalized populations. Denial of important services, for example, healthcare, education, as well as welfare benefits, has indeed occurred when digital authentication fails, effectively rendering people invisible to the state. The analysis identifies each of the dual roles as a digital identity. As per this analysis, it is both like an enabler and also like a gatekeeper. Poorly designed or implemented systems that can streamline access to public services may secure existing inequalities, encourage surveillance, as well as compromise data privacy. Democratic citizenship can be reinforced through transparent governance, through inclusive design, and through strong legal safeguards so that digital identity systems might not weaken it, as has been highlighted by the study's immediate necessity. Policymakers should have discussions on digital identity and consider it as being a social contract including rights-based obligations. They should consider it as no more than just a technological upgrade.

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5030

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Modern digital information growth now requires PDF documents for essential operations such as education research and legal applications. Browsing PDF files by hand results in inefficient time-spending processes for locating necessary information. A paper explores the development of an artificial intelligence chatbot platform that makes PDF document interactions more efficient through natural language command inputs. Through Streamlit development the system delivers an easy-to-use interface which permits users to add PDF files then immediately retrieve context-specific responses. With LangChain for document parsing and retrieval and OpenAI's large language model for response generation and FAISS for semantic similarity search the system operates in the backend. The integrated system components extract meaningful answers which directly come from document content. Users gain improved text consumption experiences through the chatbot which operates as a better and quicker substitute for standard reading methods together with search tools. The evaluation shows users are pleased with this solution as it performs efficiently with different documents.

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PDF chatbot, Streamlit, Large Language Models, LangChain, Document Retrieval, Semantic Search, Natural Language Processing

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In the world facing so many environmental issues, the need for people to understand how to live sustainably is one of the first steps we can take towards a greener world. Eco Quest is a groundbreaking, gamified web platform that sets out to revolutionize how people track and improve their environmental footprint. This platform makes sustainability a rewarding and fun experience by combining interactive aspects with data-driven insights. Eco Quest utilizes advanced technologies, including machine learning for personal recommendations, user interfaces for user-friendly interaction, data on a robust database for accurate tracking, to provide an all-in-one solution for personal environmental sustainability. It gives users actionable metrics on their energy consumption, water usage and waste generation while recommending ways to improve those habits. Using a gamification approach, the platform motivates users with tangible incentives to take action, thereby creating a community of like-minded individuals who feel compelled to together make a direct impact. Eco Quest creates a bridge between sustainability awareness and action; the platform gamifies complex sustainability goals into simple, fun tasks, ensuring that sustainable living is a way of life for a sustainable future.

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Sustainability, Carbon Footprint, Web Application, Gamification, Environmental Impact

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Recent acts of terrorism have become a serious threat to international peace and security, resulting in a severe humanitarian and economic catastrophe marked by the loss of many lives and significant financial losses. In reaction to this urgent issue, the academic community has presented a variety of AI-powered predictive analytics techniques as potential tools in the war on terrorism. Because of the growing complexity and volume of the underlying data, machine learning (ML) techniques--which are frequently used in counterterrorism ef forts--face the difficult task of properly predicting terrorist operations. Here, we offer an optimal weighted voting ensemble classifier as a solution, specifically designed for classifying the different types of weapons used in terrorist attacks.

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--Terrorism Prediction,CounterTerrorism Pre dictive Analytics , Weapon Classification ,Ensemble Classifier, Machine Learning, Particle Swarm Optimization (PSO).

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Nostalgic marketing has emerged as a powerful strategy for attracting and retaining consumers, particularly in the food and beverage industry. In Kerala, cafeterias are increasingly using nostalgia-themed marketing to evoke positive memories and create emotional connections with consumers. This study investigates the effectiveness of nostalgic marketing in attracting consumers and enhancing their satisfaction in the context of cafeteria businesses. By employing both qualitative and quantitative research methods, the study examines how nostalgia-based branding, ambience, and menu designs influence consumer behaviour. The results suggest that nostalgia has a significant positive impact on consumer attraction and satisfaction, particularly when combined with high-quality service and product offerings. A conceptual model is developed to show the relationships between independent variables (nostalgic marketing factors), mediating variables (emotional connection), and dependent variables (consumer attraction and satisfaction).

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Nostalgic Marketing, Consumer Attraction, Consumer Satisfaction, Cafeteria Business, Emotional Connection, Kerala

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The Citizenship (Amendment) Act, 2019 (CAA) has indeed ignited within it an ethical and also legal debate within India's constitutional framework. Citizenship is very fast-tracked specifically for Hindu, Sikh, Buddhist, Jain, Parsi, and Christian migrants from Pakistan, Bangladesh, and Afghanistan who entered India before December 31, 2014, because of religious persecution since this legislation amends the Citizenship Act of 1955. However, in the instance when they exclude certain Muslims and certain persecuted groups like Rohingya Muslims and Sri Lankan Tamils, they seriously concern the people in regard to religious discrimination, constitutional validity, and the secular character that the Indian state has. Criticism about the Act exists because of potentially violating Articles 14, 15, and 21 of the Indian Constitution since those do protect life and personal liberty, those do prohibit discrimination, and those ensure equality before any law. While the government defends entirely the Act because it addresses religious persecution in certain Islamic states in such a humanitarian manner, critics argue its quite selective inclusion of certain communities as well as countries does not classify reasonably under Article 14. The CAA has been greatly commended by people, from an ethical point of view, for protecting persecuted minorities, and it has also been strongly condemned for weakening India's pluralistic values. Public discourse and also judicial scrutiny do continue to be fueled by each of the constitutional, of the legal, and of the ethical dimensions of the CAA.

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2864

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This project details the development of a fully offline, privacy-centric voice assistant, utilizing a fine-tuned Large Language Model (LLM). By employing parameter-efficient fine-tuning (QLoRA), we adapt a pretrained model, such as Mistral-7B or Phi-2, on custom instruction-tuning datasets tailored for conversational tasks. The resultant model is optimized and quantized for local inference using llama.cpp and is hosted through OpenWebUI, a user-friendly interface for LLM interaction. Speech-to-text conversion is facilitated by Whisper, while Coqui TTS provides natural voice responses. The system operates entirely on local hardware, thereby ensuring user data remains private without dependence on cloud services. This architecture offers a scalable and customizable platform for intelligent, voice-first human-computer interaction in offline or low-connectivity environments

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 Keywords

Offline VoiceAssistant, Large Language Models(LLM), QLoRA,Speech Recognition, Text-to-Speech (TTS), Privacy-Preserving AI

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In this review, the information has been provided on the research studies and investigations which were reported earlier by the researchers. The main focus is based on deoxyribonucleic acid (DNA) studies of several tree species. It has been observed that isolation of pure DNA makes the base of those molecular studies which completely rely over the quality of DNA. Hence, several kind of DNA isolation protocols are available in the research world. Many are based on readily available kit methodology, modified cetyltrimethylammonium bromide (cTAB) protocols of genomic DNA isolation, etc. The major objective of these methods is to achieve quality DNA with adequate amount for best outcomes in the molecular studies.

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DNA Isolation, DNA markers, Molecular studies, Tree species

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