Volume 14 | Issue 5

 Abstract

Health and fitness is being given utmost importance these days and a simple system is needed that will allow a user to track nutrition more effectively. This paper proposes a Smart Meal Tracker that helps the user to get meal plans personalized, check nutrition values of the meals and get notified on time. The system collects basic details from user such as age, height, weight, gender and fitness goals that will be utilized to compute BMI, calorie intake for the day and protein needed. Based on these inputs the system generates the diet plan for a day with the complete nutrient composition and nutrition values of the food that needs to be eaten. One of the highlighting features of this application is that it works on the basis of location-based price of the food and makes meal suggestions based on what the user is comfortable spending in his/her region. The smart reminder feature works by providing browser notifications which is another added advantage that will prompt the user on the time of his meals to track diet more consistently. It is designed using HTML, CSS and JavaScript for the display of data to make it responsive and user-friendly and without a back-end.

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 Keywords

Smart Meal Tracker, Personalized Nutrition, Meal Planning, BMI Calculation, Calorie and Protein Estimation, Location-Based Pricing, Reminder System, Web Application, Local Storage, User Engagement

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The study provides an examination of the ensemble machine learning algo rithms alongside the techniques of uncertainty measures on nonlinear economic forecasting. We have an extremely large simulation experiment: we have 12 data generating processes (DGPs), which are threshold effects, smooth transition autoregressive models, regime switching dynamics, and chaotic systems. Together with conformal prediction and quantile regression forests, there are three ensemble methods, which include bagging, boosting, and stacking, that pro vide powerful prediction intervals. We perform performance appraisal of Monte Carlo replications of forecasting with sample sizes of 250 to 2000 observations and signal-noise ratio of 10,000 and nonlinearity of the signal of different levels with signal-noise ratios. Results have depicted stacking with conformal prediction to have great coverage probabilities (93.2-96.8) and competitive point forecast accuracy. Boosting performs optimally in high signal to noises circumstances, but loses coverage when nonlinearity is severe. Bagging achieves good baseline performance of all DGPs. Our analysis provides the outcome of the strong dominance regions of both ensembles techniques and provides effective suggestions of quantifying uncertainty in applying economic forecasting techniques. The study is also relevant methodologically by hypothetically proposing an adaptive ensemble selection framework and empirically by defining performance limits at both extremes within the non-linearity spectrum.

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Ensemble learning, uncertainty quantification, conformal prediction, quantile regression, nonlinear forecasting, Monte Carlo simulation

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This study explores how phonetic awareness contributes to the development of English language skills in multilingual classroom environments. Learners who come from varied linguistic backgrounds often face challenges related to pronunciation, sound recognition, and reading accuracy. The paper argues that an improved awareness of speech sounds supports better articulation, listening ability, and decoding skills. A descriptive qualitative research design was adopted, with data collected through classroom observations, structured questionnaires, and diagnostic assessments. The findings suggest that students with stronger phonetic awareness demonstrate greater fluency, experience less interference from their first language, and show increased confidence in communication. The study emphasizes the need to incorporate systematic phonetic instruction into language teaching practices to promote comprehensive language learning.

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This study explores how phonetic awareness contributes to the development of English language skills in multilingual classroom environments. Learners who come from varied linguistic backgrounds often face challenges related to pronunciation, sound recognition, and reading accuracy. The paper argues that an improved awareness of speech sounds supports better articulation, listening ability, and decoding skills. A descriptive qualitative research design was adopted, with data collected through cla

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Mathematics anxiety is a major academic concern that can affect students' confidence, participation, and performance in mathematics. The present study, titled "Exploring Mathematics Anxiety among Secondary School Students," aimed to examine the level of mathematics anxiety among secondary school students and determine whether it differs on the basis of gender. The study was conducted using a descriptive survey method. A total sample of 122 secondary school students was selected for the study, including 60 male and 62 female students. Data were collected through a Mathematics Anxiety Scale and analysed using descriptive statistics and an independent samples t-test. The overall findings indicated that students experienced a moderate level of mathematics anxiety. The mean anxiety score of male students was 88.98, while the mean score of female students was 87.60. Although male students showed a slightly higher mean score, the difference was not statistically significant, t(120) = 0.577, p = 0.565. Therefore, the study found no significant difference in mathematics anxiety between male and female secondary school students. The findings suggest that mathematics anxiety is experienced by both genders almost equally. Hence, teachers, parents, and school counsellors should adopt supportive teaching and counselling strategies for all students to reduce fear and improve confidence in mathematics learning.

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 Keywords

Mathematics anxiety; secondary school students; gender difference; male and female students.

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This study examines the multifaced evolution of Kalamkari,a 3000 year old Indian textile art from its origins as a sacred storytelling medium to its current status as a global symbol of sustainable fashion by analysing the two primary traditional style SRIKALAHASTI and Machilipatnam the research highlight the intricate 23 steps process involving organic dyes and manual craftsmanship The paper further explore contemporary practices where the art has transitioned from temple scrolls to high end couture, adopted by renowed designers like Sabyasachi Mukherjee and Ritu kumar this shift has introduced digital printing and diverse base fabric such as silk and georgette creating a tension between mass production and authentic hand printed heritage.

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Kalamkari, Sustainable textiles, Srikalahasti, Machilipantam, Cultural heritage, India Fashion Natural dye

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Intrusion Detection Systems (IDS) are essential elements in detecting and responding to security threats within computer systems and networks. [1].With the increasing complexity of cyber-attacks, traditional detection systems are no longer sufficient, leading to the adoption of machine learning and hybrid detection techniques [2].This paper presents an in-depth study of IDS, including detection methods, architecture, datasets, tools, experimental analysis, and future advancements [3].

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Network Security , IDS , Cyber Attacks , Machine Learning , Deep Learning , Anomaly Detection .

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:This implementation is based on computer vision technology, in this I have developed a system which will process real time video, it will detect the object in real time and track the moving object. The main objective of this system is to detect objects in real time and track that object. Some techniques have been used in this. In this system, YOLO algorithm has been used for object detection and this implementation is based on computer vision. Python language has been used for programming. Deep Sort algorithm has been used for tracking. By using this technique, the object is identified and its framing is done and after this the identified object is put for tracking. Then the DeepSort algorithm gives a different ID to each object and tracks the moving object. After completing this system, a system was developed which Real time object detected and tracked

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computer vision ,yolo ,deepsort ,real tim system ,opencv

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This study evaluated the e-governance services among cities in Central Luzon in terms of: ease of use, accessibility, efficiency, reliability, and responsiveness. It also identified the best practices and their problems encountered on delivering their e-government services. To answer this, the researcher employed a Quantitative Research this is appropriate since the study measured perceptions and examined relationships using numerical data collected from a defined group of respondents. The study reveals that all indicators used to measure e-governance services were rated as agree. However, efficiency obtained the highest mean of 3.99 while ease of use and responsiveness obtained the lowest mean of 3.95. The overall mean of 3.96 across five dimensions was obtained which suggests that respondents generally view digital government platforms as functional and beneficial in facilitating government transactions. The results imply that citizens can access services through digital systems and complete various transactions without relying entirely on traditional in-person procedures. The growing use of online platforms in public administration reflects the broader transition toward digital governance, where technology plays an important role in improving service accessibility and administrative efficiency. In terms of the problems encountered, "Slow system performance affects transactions" was considered as the topmost problems encountered by the respondents with a frequency of 375 or 98.68%. While the least problem raised was the "Data Privacy and security concerns are present" with a frequency of 230 or 60.53%. It should be noted that understanding these concerns is important because the success of digital government services depends not only on the availability of technology but also on how well these systems function in practice. When users encounter recurring problems during transactions, their overall perception of digital government initiatives is affected. Considering all these findings, certain recommendations were proposed, such as reviewing regular transaction procedures to remove unnecessary steps and reducing delays, particularly during busy periods. Improved coordination among offices can also help speed up processing time and continuously monitoring and updating their current system to maintain stable system performance and sustain user trust.

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Accessibility, Administrative efficiency, best practices, Citizen perception, Digital platforms, Ease of use, Efficiency, E-governance, Government transactions, Implementation challenges, Reliability, Responsiveness, Service delivery, System performance, User experience.

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Warli art, a traditional tribal art form originating from the Warli community of Maharashtra, India, serves as a visual narrative of the community's socio-cultural life and evolving history. Characterized by its distinctive use of geometric shapes and monochromatic palette on mud walls, Warli paintings historically depicted themes of nature, agriculture, rituals, and communal harmony. However, over time, this art form has transformed significantly, reflecting broader social and historical changes. This study explores how Warli art has adapted to shifting contexts such as urbanization, globalization, and increased interaction with mainstream society. Contemporary Warli artists have begun incorporating modern elements including technology, environmental concerns, education, and social issues into their work, while still retaining traditional motifs and techniques. These changes highlight the dynamic nature of Warli art as both a cultural expression and a medium of storytelling. Furthermore, the transition of Warli art from ritualistic wall paintings to commercial canvases and global markets illustrates changes in economic structures and cultural preservation strategies. While commercialization has provided new opportunities for artists, it also raises questions about authenticity and cultural identity. By examining traditional and contemporary Warli artworks, this paper underscores how Warli art not only preserves the heritage of the community but also acts as a living document of social transformation and historical progression.

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Tradition, modernization, tribal life, urbanization, cultural change, rituals, nature, symbolism, globalization, heritage, transformation.

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The healthcare sector's rapid digitization has fundamentally transformed the delivery of medical services, enabling efficient data management, real-time patient monitoring, and improved clinical outcomes. Technologies such as Electronic Health Records (EHRs), telemedicine platforms, and cloud-based health systems have significantly enhanced accessibility and operational efficiency. However, this digital evolution has also expanded the sector's vulnerability to cyber threats. The highly sensitive nature of patient data, combined with reliance on interconnected digital infrastructure, makes healthcare institutions particularly attractive targets for cybercriminals. Incidents such as ransomware attacks, phishing schemes, and large-scale data breaches not only result in financial losses but also disrupt critical healthcare services and compromise patient safety. In this context, Cyber Risk Insurance has emerged as an essential risk transfer and mitigation mechanism, enabling healthcare organizations to manage the financial and operational consequences of cyber incidents. This study undertakes a comprehensive comparative analysis of cyber risk insurance frameworks in India, the United States, and the European Union. It critically examines how regulatory regimes particularly the HIPAA, GDPR, and Digital Personal Data Protection Act, 2023 influence the design, scope, and effectiveness of cyber insurance policies within the healthcare sector. Using a doctrinal and comparative research methodology, the study analyzes key dimensions such as policy coverage, exclusions, underwriting practices, premium determination, and claims settlement mechanisms. It further explores the role of regulatory enforcement, market maturity, and technological integration, including the adoption of InsurTech solutions, in shaping cyber insurance ecosystems. The analysis reveals significant disparities across jurisdictions: while the United States and the European Union demonstrate relatively mature, standardized, and well-regulated cyber insurance markets, India's framework remains in a developmental phase, characterized by regulatory ambiguity, limited actuarial data, and inconsistent policy structures. The article concludes that strengthening cyber risk insurance in India requires a multi-faceted approach, including regulatory harmonization, policy standardization, enhanced enforcement of data protection laws, and increased integration of advanced technologies. These reforms are essential to building a resilient healthcare cybersecurity ecosystem capable of effectively addressing evolving cyber threats.

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Cyber Risk Insurance, Healthcare Cybersecurity, Data Breach Liability, GDPR, HIPAA, InsurTech, Comparative Law, India

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Protective discrimination has been one of the most debated features of the Indian constitution. Designed primarily to correct historical injustices and structural inequalities rooted in caste hierarchy and social exclusion, reservation policies have played a crucial role in shaping India's social justice agenda. The framers of the Constitution, particularly B. R. Ambedkar envisioned affirmative action as a mechanism to ensure substantive equality rather than merely formal equality. However over time, the policy has generated intense debates regarding merit, efficiency, and political instrumentalisation. This paper critically examines the philosophical foundations, constitutional provisions, advantages, criticisms, and legal evolution of affirmative action in India. It also analyses contemporary data to assess whether reservation policies have significantly contributed to social mobility and representation among historically marginalized communities. By examining key judicial pronouncements such as the Indra Sawhney case and constitutional developments including the 103rd Constitutional Amendment introducing Economic Weaker Section (EWS) reservation for economical backward, the study evaluates how the concept of positive discrimination has evolved. The paper concludes by suggesting reforms that can balance social justice with merit-based competition while strengthening the broader goal of an inclusive democratic society.

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Reservation Policy, Indian Constitution, Social Justice, Meritocracy, Equality, Positive Action

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Landslides are one of those disasters that don't always make headlines, but when they do, the damage is often devastating--especially in mountainous regions like the Himalayas or the Western Ghats. In most cases, heavy or prolonged rainfall quietly builds up pressure within the soil until, suddenly, the slope gives way. Predicting such events is not straightforward, mainly because rainfall behaves differently across regions, and terrain conditions vary just as much. In this work, we propose a Region-Adaptive Artificial In- telligence Framework (RAAF) that attempts to handle this complexity more realistically. Instead of relying on a single snapshot of rainfall, the model looks at how rainfall evolves over time--capturing short-term bursts as well as longer-term accumulation. To do this, we combine Convolutional Neural Networks (CNN) for understanding spatial terrain patterns with Long Short-Term Memory (LSTM) networks that learn how rainfall changes over days or even weeks. One practical challenge we focused on is that models trained in one region often fail in another. To address this, we incorporate a transfer learning mechanism that allows the model to adapt using limited data from new regions. Our results show that combining multi-temporal rainfall data with region-adaptive learning improves prediction accuracy to over 91

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Landslide Prediction, Deep Learning, CNN- LSTM, Multi-Temporal Rainfall, Transfer Learning, Disaster Management

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Abstract The bifurcation of Indian states, though intended to improve governance and meet local aspirations, can catalyse significant environmental and ecological challenges. This is acutely evident in the case of Uttarakhand, formed in 2000 from Uttar Pradesh, which occupies a critical swath of the Himalayas--an ecologically fragile and vital region. This paper critically examines the adverse impacts on Uttarakhand's environment post-bifurcation, focusing on deforestation, biodiversity loss, glacial and riverine stress, heightened disaster vulnerability, and governance challenges. The analysis underscores the need for urgent institutional reform, participative environmental governance, and development models that align with the unique vulnerabilities of the Himalayan ecosystem.

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Wildlife, forest

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Crop diseases significantly impact agricultural productivity and food security worldwide. Early and accurate identification of crop diseases is crucial for effective management and prevention of yield loss. Traditional methods of disease detection rely heavily on expert knowledge and manual inspections, which are timeconsuming, subjective, and often inaccessible in remote areas. To overcome these limitations, this study explores the use of deep learning models for the automatic identification of crop diseases. By leveraging convolutional neural networks (CNNs), a widely used deep learning architecture, we trained a model on a publicly available dataset of infected and healthy crop leaf images. The system was designed to classify multiple types of diseases across various crop species with high accuracy. The model was trained and validated using techniques like data augmentation, transfer learning, and fine-tuning of pre-trained networks (e.g., VGG16, ResNet50) to improve performance and generalisation. The proposed deep learning-based approach offers a scalable, efficient, and low-cost solution for crop disease detection that can be integrated into mobile applications or deployed on IoT devices for real-time monitoring. This system holds great potential for empowering farmers with actionable insights, reducing dependence on expert intervention, and improving overall crop health management practices.

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Crop Disease Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Precision Agriculture, VGG16, ResNet50, Smart Farming, Plant Health Monitoring, Transfer Learning.

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In recent years, the rapid growth of Android applications has intensified the need for secure mobile software, while many existing vulnerability detection techniques still depend heavily on manual review and runtime testing. The proposed Static Analyzer of Vulnerabilities in Android Applications seeks to automate this task by introducing a static analysis framework that identifies insecure coding practices directly from decompiled APK files. Unlike traditional dynamic scanners, the system applies regex-based and rule-driven inspection to application source code, manifest files, and Smali components without executing the app. The analysis core is implemented using Python and integrates reverse engineering tools such as JADX and APKTool for efficient extraction and processing. Detected issues, including API misuse, hardcoded credentials, and permission misconfigurations, are automatically consolidated into structured HTML and JSON reports. A lightweight interface enables developers to upload APKs and review categorized vulnerability findings with ease. Rule-based filtering is used to reduce false positives and improve detection reliability. Validation on benchmark datasets demonstrates that the framework achieves strong accuracy, scalability, and low computational overhead. By combining automated static analysis with clear and intuitive reporting, the proposed approach streamlines Android vulnerability detection into a fast, accessible, and developer-friendly security assessment process.

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Static Analysis, Android Security, Regex-Based Detection, Rule Engine, APK Decompilation, Vulnerability Reporting.

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ABSTRACT - An Intelligent Emergency Health Monitoring and Smart Doctor Dispatch System is an advanced healthcare solution designed to enhance rapid response and decision-making during medical emergencies. The system integrates artificial intelligence, Internet of Things (IoT) sensors, and real-time data analytics to continuously monitor a patient's vital signs such as heart rate, blood pressure, oxygen saturation, and body temperature. By analyzing these parameters, Med altert AI can detect abnormal patterns and predict potential health risks before they become life-threatening .The core feature of Med altert AI is its intelligent alert mechanism, which automatically notifies emergency services, caregivers, and nearby medical professionals when critical conditions are identified. Unlike traditional emergency systems that rely on manual reporting, this platform ensures instant, accurate, and automated communication. The smart doctor dispatch module uses AI-based algorithms to locate and assign the most suitable and nearest available medical professionals based on specialization, availability, and distance, significantly reducing response time. Med altert AI also includes a centralized dashboard that allows hospitals and emergency responders to access real-time patient data, medical history, and location information. This enables healthcare providers to prepare appropriate treatment even before reaching the patient. The system is designed to be scalable and adaptable, making it suitable for hospitals, elderly care centers, remote areas, and home healthcare environments .By combining continuous health monitoring, predictive analytics, and intelligent resource allocation, Med altert AI aims to minimize delays in emergency care, improve patient survival rates, and reduce the burden on healthcare infrastructure. It represents a step toward smarter, faster, and more reliable emergency healthcare services, ensuring that patients receive timely medical attention with precision and efficiency.

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Keywords:Intelligent Healthcare System, Emergency Health Monitoring, Smart Doctor Dispatch, Artificial Intelligence (AI), Internet of Things (IoT).

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Inclusive education has become an important concern in Indian higher education, particularly after the National Education Policy 2020 and the Rights of Persons with Disabilities Act, 2016. The present paper examines the relevance of Universal Design for Learning (UDL) as a practical framework for making higher education more accessible, flexible, and learner-centred. The study is descriptive and analytical in nature and is based on secondary sources such as policy documents, books, reports, and scholarly literature. The paper argues that inclusion cannot be achieved only through admission or reservation; it also requires changes in curriculum, classroom practices, assessment, infrastructure, and teacher preparedness. UDL offers a useful approach because it encourages multiple ways of presenting content, engaging learners, and assessing learning. The paper concludes that UDL can strengthen inclusive education in Indian higher education if it is supported by teacher training, accessible infrastructure, institutional policy, and a positive attitude towards learner diversity.

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- Universal Design for Learning, Inclusive Education, Higher Education, NEP 2020, Disability, Accessibility, Teacher Education.

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Teacher involvement in discipline management is a critical determinant of student discipline and the attainment of positive educational outcomes. Existing literature identifies teacher engagement as a central input in effective school management. In Kisumu West Sub-County, student indiscipline has remained a concern, with an average of 85 students suspended annually between 2020 and 2022, compared to the Kisumu County average of 75. Against this backdrop, the study sought to establish the relationship between teacher involvement in discipline management and the achievement of student discipline in public secondary schools in Kisumu West Sub-County, Kenya. Specifically, the study examined the relationship between teacher preventive, interventive, and restorative involvement in discipline management and student discipline outcomes. The study adopted a correlational research design and was grounded in the learners' discipline management model, which emphasizes strong teacher-student relationships, anticipatory classroom management, addressing root causes of indiscipline, regenerative approaches, and learner responsibility for behavior. The target population comprised 39 principals, 39 deputy principals in charge of administration, 542 teachers, 1,980 student council members, and one Sub-County Quality Assurance and Standards Officer (SCQASO). Using Krejcie and Morgan's sampling table, a sample of 225 teachers, 322 student council members, 35 principals, and 35 deputy principals was selected. Stratified random sampling was used for teachers and student councils, census sampling for principals and deputy principals, and purposive sampling for the SCQASO. Data were collected using questionnaires and interviews, with validity ensured by experts from Maseno University and reliability established through pilot testing and Cronbach's alpha. Findings revealed that preventive teacher involvement was highly practiced, with teachers reporting strong engagement in motivating learners, team building, and social skills development. Students also affirmed practices such as arriving in school on time and attending all lessons. Preventive involvement showed a strong positive relationship with student discipline (r = 0.879, p < 0.01). Interventive involvement, including parental engagement and consistent punishment, also demonstrated a significant positive relationship with discipline (r = 0.654, p < 0.01). Similarly, restorative involvement such as counseling and learner reintegration was positively associated with student discipline (r = 0.551, p < 0.01). Similarly, school principals, deputy principals and SCQASO observed that teacher preventive, interventive, and restorative involvement in discipline management had a huge impact in shaping the student discipline outcomes. The study concludes that while all three approaches are important, preventive strategies exert the strongest influence on discipline outcomes, followed by interventive and restorative practices. It recommends strengthening motivational and preventive programs, ensuring consistency and parental collaboration in interventions, and mainstreaming restorative practices such as counseling and reintegration, while further research should examine contextual differences across schools and the long-term academic and social effects of restorative discipline approaches

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Teacher Involvement, Discipline Management, Student Discipline, Preventive, Restorative, Interventive, Involvement

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This paper examines the emergence of Nepal-China cultural connections and their implications for India-Nepal relations. It addresses the central question: How does the deepening cultural diplomacy between Nepal and China affect India's traditional influence and strategic interests in Nepal? The study synthesizes historical and contemporary evidence to show that Chinese cultural diplomacy - encompassing Confucius Institutes, language programs, scholarships, media presence, tourism promotion, and Belt and Road Initiative (BRI) symbolism - has intensified in Nepal since the 2000s. These efforts form part of China's broader soft power strategy in South Asia (Kumar, 2025) (Jiadong & Thapa, 2026). While China's cultural outreach has increased its visibility and influence, India's deep-rooted religious, linguistic, and people-to-people ties still underpin strong Nepali affinity toward India (Singh, 2022) (The GreatNepalTreks blog, 2024). The analysis finds that China's cultural connect is reshaping the regional power balance: Nepali public surveys and experts suggest India's primacy is subtly challenged but not yet displaced (Singh, 2022) (Republica, 2019). By examining case studies (e.g. Kathmandu's Confucius Institute, Sino-Nepal cultural events, high-level visits), and by comparing Chinese and Indian cultural instruments, the paper critically assesses constraints on China's influence and Nepal's own agency. Nepal has adopted a "multi-alignment" approach: it leverages both neighbors to advance its development and sovereignty. Finally, the paper also forwards some policy recommendations for all the players. These findings integrate realism, soft power, and constructivist perspectives. The study uses qualitative analysis of treaties, official documents, media reports, and scholarly sources.

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Nepal-China relations, cultural diplomacy, soft power, India-Nepal ties, Belt and Road Initiative, Confucius Institute.

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Hangwoman : Everyone Loves a Good Hanging is a Malayalam novel written by K R Meera. The novel was published as a book by DC Books in 2012.It was translated by J Devika into English. In Malayalam title the novel was named as ' Aarachar'. The novel was originally serialized in Madhyamam Weekly in continuous 53 volumes. The English publisher of the novel was Hamish Hamilton. The novel brought Kerala Sahithya Acadami Award (2013) , Odakkuzhal Award (2013), Vayalar Award(2014), Kendra Sahithya Acadami Award(2015), Muttathu varky Award in 2018.It was short listed for the 2016 DSC Prize for South Asian Literature.

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Feminism, Patriarchy, Stereotypes, Hangwomen

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