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For many researchers, one of the biggest concerns in academic publishing is the cost involved. Article Processing Charges (APC) in some journals can be quite high, making it difficult for students and early-career scholars to publish their work. However, there is good news that many journals offer free publication without any APC allowing researchers to share their work without financial pressure. Take a look on about free publication journals without APC.

Submit Your Paper  / Check Publication Charges

What Are Journals Without APC?

Journals without APC are platforms that do not charge authors any fees for submitting, reviewing, or publishing their research papers. These journals are often funded by universities, research institutions, academic societies, or government organizations. Their main goal is to promote knowledge sharing rather than generate profit.

In many cases, these journals follow a traditional publishing model where readers or institutions cover the cost through subscriptions. Some also follow a fully open model where both authors and readers can access content freely.

Why Choose Free Publication Journals?

Publishing in journals without APC offers several important benefits:

• No Financial Burden – Researchers can publish their work without worrying about high fees.
• Equal Opportunity – Students and independent researchers get a fair chance to contribute to academic literature.
• Focus on Quality – Selection is based on research merit rather than payment capability.
• Academic Growth – Helps beginners build their research profile and gain experience in publishing.

Points to Check Before Submission

While free journals are beneficial, it is important to ensure they are genuine and reliable. Keep these points in mind:

• Confirm that the journal follows a proper peer-review process
• ISSN and DOI approval as per the policies
• Check if it is indexed in recognized academic databases
• Read the submission and formatting guidelines carefully
• Avoid journals that promise very fast or guaranteed publication
• Being careful at this stage helps protect your research and academic reputation.

Sometimes researchers believe that free journals are of low quality. This is not true. In fact, several well-established journals do not charge any APC and maintain high academic standards. The key difference lies in their funding model, not in their quality.

Free publication journals without APC provide a valuable opportunity for researchers to publish their work without financial stress. With proper research and careful selection, you can find reliable platforms that match your field and maintain academic credibility. Focus on originality, follow the guidelines, and choose wisely, your research deserves to be shared without unnecessary costs.

Published by: Neeraj Mishra

Authors: Dr. Pawan Bhaladhare, Vaibhav Ingle, Sakshi Phatake, Rushi Jagtap

Abstract: The rapid growth of internet users has led to an increase in phishing attacks, where attackers create deceptive URLs to steal sensitive information. This study presents an ensemble machine learning framework for detecting phishing websites using Natural Language Processing (NLP) and multiple classifiers, including Artificial Neural Networks (ANN), Naïve Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM). By extracting key features from URLs and applying machine learning techniques, the proposed model enhances detection accuracy. Comparative analysis demonstrates its effectiveness, achieving 98.4% accuracy in distinguishing phishing sites from legitimate ones. This approach offers a proactive solution to mitigate online security threats and protect users from cyber fraud. Phishing attacks have become more sophisticated, using deceptive URLs to target unsuspecting users. This research introduces a hybrid machine learning-based detection model that enhances accuracy through an ensemble of classifiers. The system utilizes Natural Language Processing (NLP) to extract critical URL features, which are then analyzed using Artificial Neural Networks (ANN), Naïve Bayes (NB), Random Forest (RF), and Support Vector Machines (SVM). Machine learning techniques are particularly effective in detecting zero-hour phishing attacks and adapting to emerging threats. Our implementation achieved a 98.4% accuracy in classifying websites as phishing or legitimate.

Published by: vikaspatanker

Authors: Sachin Kalmani, Pratham Shinde

Abstract: Sentiment analysis has become an essential technique for extracting actionable insights from user-generated content on e-commerce platforms. This study presents a comparative analysis of machine learning and lexicon-based approaches for sentiment classification of Amazon India product reviews, where sentiment labels are derived from user star ratings. Three machine learning models — Naive Bayes, Logistic Regression, and Random Forest — are evaluated alongside two lexicon-based methods, VADER and TextBlob. Text preprocessing and feature extraction are performed using standard natural language processing (NLP) techniques combined with TF-IDF vectorization. Models are tested under four train-test split configurations (80-20, 60-40, 40-60, and 20-80) to systematically assess the effect of training data size on performance. Results show that machine learning models consistently outperform lexicon-based approaches across all evaluation metrics. At the 80-20 split, Random Forest achieves the highest accuracy of 96.22%, followed by Logistic Regression at 85.97% and Naive Bayes at 80.58%. Lexicon-based methods plateau near 73-74% accuracy across all split configurations, confirming their insensitivity to training data volume. A notable finding is that at reduced training sizes, Naive Bayes (69.65% at 20-80) underperforms both VADER (74.11%) and TextBlob (72.90%), suggesting that lexicon-based methods are more reliable when labelled training data is scarce. These findings offer practical guidance for model selection in real-world sentiment analysis applications.

DOI: https://doi.org/10.5281/zenodo.19625686

Published by: vikaspatanker

Authors: Rahul Suthar, Navneet Kumar Singh

Abstract: Instagram is widely used by handicraft sellers be- cause it offers a visual showcase for their products. However, Instagram lacks a proper product catalogue and easy inventory tracking. This paper proposes InstaCraft, a simple web CMS built with the MERN stack (MongoDB, Express.js, React.js, Node.js). InstaCraft provides a responsive product catalogue, a basic admin interface for CRUD operations on products, and direct messaging links (WhatsApp click-to-chat and Instagram profile links). The frontend is designed for mobile devices, and images are optimised (resized, converted to WebP, lazy-loaded) to reduce load times. The paper includes TikZ diagrams of the system architecture and data flow, along with example REST API and schema code. Preliminary testing indicates that InstaCraft pages load approximately 30% faster than a raw Instagram feed displaying similar content. Feature tables compare In- staCraft, Instagram-only selling, and full e-commerce platforms. InstaCraft offers a practical middle ground: it requires less effort than a full online store while providing more organisation than Instagram alone.

Published by: vikaspatanker

Authors: Sree Vishal G, Sarika K, Dr. K. Geetha

Abstract: The rapid advancement of digital technologies and the widespread use of social media platforms have significantly influenced the way individuals present themselves in modern society. Personal appearance, grooming, and aesthetic presentation have become essential aspects of self-expression and identity. However, selecting appropriate styles, outfits, and visual themes that align with individual preferences and current trends remains a complex and time-consuming task. Traditional approaches rely heavily on manual browsing, personal judgment, and external opinions, which often lack accuracy, consistency, and personalization.To address these challenges, this paper presents Aesthetiq, an Artificial Intelligence-based aesthetic analysis and personalized styling recommendation system. The proposed system is designed as a web-based application that leverages machine learning techniques to analyze user inputs such as facial images, style preferences, and visual attributes. The system performs preprocessing, feature extraction, and classification to identify suitable aesthetic categories and generate personalized recommendations.The architecture of the system consists of a frontend interface for user interaction, a backend server for processing and communication, and an AI module for performing analysis. The database stores user data, input images, and analysis results to enable efficient retrieval and history tracking. The system ensures real-time processing and provides visually interpretable outputs through an interactive dashboard.Experimental evaluation indicates that the proposed system achieves improved accuracy and performance compared to traditional methods. The system enhances user decision-making, reduces effort, and provides tailored recommendations that align with individual preferences and modern trends.

DOI: https://doi.org/10.5281/zenodo.19614962

Published by: vikaspatanker

Authors: MD Jaynul Abedin, Md Tayef Shiham

Abstract: Controlled-environment agriculture is rapidly becoming data-intensive and cyber-physical with the rapid digitalization of controlled-environment greenhouses. With artificial intelligence, IoT frameworks, and robotic surveillance systems becoming integrated in greenhouse operations, algorithms are playing a larger role in the managerial decision-making process instead of human supervisors alone. This change opens the idea of algorithmic management to the world of agricultural workforce – a field that has not been sufficiently investigated in the existing studies. This paper constructs a socio-technical system to examine the effect of the algorithm systems on workforce scheduling, performance tracking, and coordination of operations in the greenhouse environment. An optimization model in mathematics is presented to structure task distribution based on efficiency, fairness, and worker fatigue where multi-objective scheduling can be used to achieve productivity and human well-being. The paper offers a proposed structured simulation dataset and a survey instrument to help assess worker perceptions of surveillance and autonomy and fairness to support future empirical research. A comparative analysis of traditional and algorithmic management models indicates that there are trade-offs between agency and precision of operations and labor. The results emphasize that algorithmic management in the agricultural sector is not an issue of technical improvement but a governance problem that needs to be transparent, accountable, and human-centered. This study forms a conceptual and analytical base of ethically responsible AI-based workforce management in smart greenhouse settings and adds to the discussion on the future of human-AI collaboration in industrial systems.

DOI: https://doi.org/10.5281/zenodo.19614456

 

Published by: vikaspatanker

Authors: S.Senthazhai, V.Kokila, R.Dharshini, B.Pragathi, E.Sonashriyaa

Abstract: This paper presents the design and implementation of a smart environmental monitoring and control system using the Raspberry Pi Pico W microcontroller. The proposed architecture integrates multiple sensors—including temperature and humidity, gas level, water level, and feeder level—to continuously monitor ambient conditions. A forecasting module enhances system intelligence by predicting short-term environmental trends based on real-time data. The Raspberry Pi Pico W processes sensor inputs and communicates wirelessly with a cloud database, enabling remote access via mobile or desktop interfaces. Relay-controlled actuators such as a heater, cooling fan, exhaust fan, water pump, and servo motor respond dynamically to sensor thresholds, ensuring automated regulation of the environment. The system demonstrates a scalable and cost-effective solution for applications in smart agriculture, pet care, and automated home ecosystems. Experimental results validate the system’s responsiveness and reliability, highlighting its potential for real-world deployment in IoT-based automation frameworks.

DOI: https://doi.org/10.5281/zenodo.19608972

 

Published by: vikaspatanker

Authors: Dr. Jyoti Prakash

Abstract: Ruminant animals have a highly specialized microbial ecosystem within their rumen, allowing for the digestion of complex plant material such as cellulose. This mutualistic relationship not only provides for the nutritional requirements of the host animal but is also essential for ecosystem functioning. The rumen microbes play a large part in carbon and nitrogen cycling, but as a byproduct of anaerobic fermentation, methane is produced (Moss et al., 2000). Although methane production is a concern for global warming, ruminant animals are essential for the production of nutrient-dense foods from low-quality feedstuffs. This article will discuss rumen microbial ecology, its importance for ecosystem functioning, its contribution to climate change, and its importance for sustainable agriculture.

DOI: https://doi.org/10.5281/zenodo.19607530

Published by: vikaspatanker

Authors: Sandeep Kumar Kashyap, Shweta Vikram

Abstract: High-precision GNSS Real-Time Kinematic (RTK) positioning often suffers from gross errors caused by non-line-of- sight (NLOS) multipath and other anomalies, which can dramatically bias simple coordinate averages. This paper presents Entropic-Topological Barycentric Synthesis (ETBS), a novel framework that dynamically selects a reliable subset of GNSS coordinates and computes a weighted barycentric average. The method proceeds in phases: (1) Topological filtering of the raw point set using kernel density estimation to identify and remove outliers; (2) Entropy weighting of remaining points based on multiple quality metrics (e.g. carrier-to-noise ratio, PDOP, satellite elevation variability) to assign higher weight to more reliable observations; and (3) Barycentric coordinate synthesis by computing the Wasserstein (transport) barycenter of the weighted points, yielding the final coordinate estimate. In synthetic tests mimicking open-sky and harsh urban conditions, ETBS consistently isolates outliers and yields centimeter-level accuracy, whereas traditional mean/median or robust least-squares methods produce errors on the order of decimeters or more. The results demonstrate that ETBS effectively neutralizes extreme outliers and achieves superior positioning precision.

DOI: https://doi.org/10.5281/zenodo.19606990

Published by: vikaspatanker

Authors: Ms. Babita, Dr. Brij Mohan Goel

Abstract: Cancer will continue to be a leading cause of mortality worldwide, making early detection and timely intervention essential for improving survival rates. This study will propose a hybrid Artificial Intelligence (AI)-based healthcare framework for early cancer detection and preventive analysis using deep learning techniques. The model will integrate Convolutional Neural Networks (CNN) for medical image feature extraction and Long Short-Term Memory (LSTM) networks for analyzing sequential clinical data.The system will be evaluated on benchmark cancer datasets using performance metrics such as accuracy, precision, recall, and F1-score. The proposed hybrid model is expected to outperform traditional machine learning approaches by achieving higher accuracy and lower error rates.The framework will support early-stage diagnosis, risk prediction, and personalized preventive strategies. Although challenges such as computational complexity and data privacy will persist, the proposed system is anticipated to offer strong potential for real-world healthcare applications and contribute to AI-driven cancer care.

Published by: vikaspatanker