Category Archives: Uncategorized

Authors: Mrs. M. Sujana Priyadarshini, Vinnakoti Sakyavardhan

Abstract: Brain tumour diagnosis using Magnetic Resonance Imaging (MRI) plays a crucial role in early treatment planning and patient survival. However, manual interpretation of MRI scans is time-consuming and may lead to inconsistent clinical decisions. To address these limitations, this study proposes an explainable ensemble transfer learning framework for multiclass brain tumour classification. The proposed model integrates multiple pre-trained convolutional neural network architectures and aggregates their predictions using an ensemble strategy to enhance classification robustness and reduce overfitting. Furthermore, Explainable Artificial Intelligence (XAI) techniques are incorporated to visualize tumour regions and improve model interpretability, thereby increasing clinical trust and reliability. The dataset consists of multiclass MRI images categorized into glioma, meningioma, pituitary tumour, and no-tumour classes. Data augmentation and preprocessing techniques are employed to improve generalization performance. Experimental evaluation demonstrates that the ensemble framework achieves superior classification accuracy compared to individual transfer learning models. Performance is assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. The integration of explainability tools further validates the model’s capability to focus on clinically relevant tumour regions. The proposed approach offers a reliable, scalable, and interpretable solution for automated brain tumour detection and classification, making it suitable for real-world clinical decision support systems.

DOI: https://doi.org/10.5281/zenodo.19062217

Published by: vikaspatanker

Authors: Sai Narendra Ghodke, Siddhartha V. Bhosale, Sunraj Shetty

Abstract: This paper presents APEX AI, a professional-grade equity trading signal platform designed for National Stock Exchange (NSE) listed Indian stocks. The system employs a heterogeneous ensemble of three complementary machine learning models: Gated Recurrent Unit (GRU) networks for sequential pattern capture, Temporal Convolutional Networks (TCN) for multi-scale temporal feature extraction, and LightGBM for gradient-boosted tabular learning. These models are fused through a soft-voting ensemble to produce probabilistic price forecasts expressed as P10, P50, and P90 quantile estimates over a 14-day horizon. A four-stage gate architecture governs signal quality, filtering signals based on trend alignment, volatility regime, volume confirmation, and risk-adjusted expected return. The platform exposes predictions through a FastAPI backend and a React/TypeScript/Vite frontend featuring a TradingView-style candlestick chart with an integrated forecast cone. Experimental evaluation on historical NSE data demonstrates directional accuracy above 62%, with the ensemble outperforming any individual constituent model.

 

 

Published by: vikaspatanker

Authors: Byragoni Srinivas, N.Sriaknth

Abstract: This project gives in brief, the theory behind the design of liquid retaining structure. Water tanks are storage containers for storing water. Elevated water tanks are constructed in order to provide required head so that the water will flow under the influence of gravity, the construction practice of water tanks is as old as civilized man. The water tanks project has a great priority as it serves drinking water for huge population from major metropolitan cities to the small population living in towns and villages. The main aim of this project is to understand the behavior of elevated water tank by observing the results of Bending Moment, Shear Forces, Maximum Stress, and Maximum Displacement and Design by using BIM software.

 

 

Published by: vikaspatanker

Authors: Mrs. T.Swapna Sridevi, Peddireddy Pattabhi Rama Lingeswar

Abstract: The rapid growth of digital communication platforms has significantly increased the volume of SMS and email messages exchanged daily. While these technologies enhance connectivity and information sharing, they have also become primary channels for spam, phishing, and fraudulent activities. Spam messages not only cause inconvenience but also pose serious security and privacy risks to individuals and organizations. Therefore, developing an accurate and efficient automated spam detection system has become an essential requirement. This study proposes a robust machine learning framework for intelligent classification of spam and legitimate (ham) SMS and email messages using advanced text analytics techniques. The system incorporates comprehensive preprocessing methods, including text cleaning, tokenization, stop-word removal, and normalization, followed by feature extraction using techniques such as TF-IDF and word embeddings. Multiple machine learning algorithms, including Naïve Bayes, Support Vector Machines, Logistic Regression, Random Forest, and Gradient Boosting, are implemented and comparatively evaluated. To further enhance predictive performance, ensemble learning strategies are employed to combine the strengths of individual classifiers. Experimental results demonstrate that the proposed hybrid framework achieves high accuracy, precision, recall, and F1-score across benchmark datasets. The system effectively minimizes false positives and false negatives, thereby improving reliability in real-world applications. The proposed approach contributes to the development of scalable, intelligent, and adaptive spam filtering systems capable of handling evolving spam patterns in modern communication networks.

DOI: https://doi.org/10.5281/zenodo.19061522

Published by: vikaspatanker

Authors: Dr Y Subba Reddy, A Guru Jyotshna, K Deepthi, B Paramesh, D.Siva Ganga Keerthi

Abstract: Neuroplasticity, as the name suggests, refers to the brain's remarkable ability to reorganize itself by forming new connections throughout life. Neuroplasticity has been observed to be more active in early childhood, as the processes of synaptic pruning and myelination are more active during this period. Research has shown that environmental stimulation has a direct effect on the thickness of the cortex, as well as the dendritic branching patterns of the neurons. Functional magnetic resonance imaging has shown that the brains of adults have a lot of plasticity, which enables the brains to recover from injury as well as to learn new skills. The neuroplasticity framework has a lot of implications, especially in the field of educational psychology as well as rehabilitation medicine. Experimental results using crawled Tor URL datasets demonstrate that the proposed method achieves 97.50% accuracy, outperforming conventional CNN-based deep fingerprinting techniques. Further optimization is achieved by incorporating a BiGRU layer after LSTM, enabling bidirectional feature extraction and improving prediction performance to 97.86%. Performance metrics including precision, recall, F1-score, and confusion matrices confirm the enhanced effectiveness of this methodology for distinguishing normal and attack-type Tor services, providing a robust framework for secure network monitoring.

DOI: https://doi.org/10.5281/zenodo.19060572

Published by: vikaspatanker

The working professionals, scholars, and the students pursuing their academics in the field of food science, which is the subject of our daily lives and needs daily up to date information and sources to be covered in the research, are making a great contribution among the community. But here is a big task for those who cannot pay a high charge for publication. To resolve this problem, we will walk through the detailed information of food science journal free publication and how this helps scholars to get published their paper for free.

Submit Your Paper  / Check Publication Charges

What Are Food Science Journals?

The journals prove to be very helpful for the students who are not getting funded or the independent scholars who cannot afford a high expensive fee. The journals are basically funded by govt. organizations, research institutes or college and universities, covering branches of food science as food microbiology, food processing, nutrition, genomics and food science as a multi-disciplinary field etc.

How these journals help scholars to get published their research paper for free?

To get a free publication opportunity you need to know that it fully depends on the research paper you prepared. Yes, here the quality of your paper matters most to pass the basic standards of journal paper publication, written with honesty, carries the great feedback result of review, do not carry plagiarism and should not be copied.

To write well and get more knowledge about your topic or subject you are preparing, look for the free reading access providing journal where you can dig deep in pre published articles and include in your survey paper. With these traits do visit the official websites, not every journal is true every time, they may carry some hidden charges.

You can actively check for the following qualities of the journals which do not charge for publication;

• Confers proper submission process guidelines with transparency
• Authorized and recognized widely and free reading access of pre published articles
• Double blinded or peer review process with subject experts
• Serve International Standard Serial Number and Digital Object Identifier which depends on the paper quality and journal general policies
• A well name, designation and publication mentioned digital certificate
• Fast publication capacity and copy right form
• A well-organized and cooperative paper formatting team to make the paper more structured

However sometimes it takes a long to publish the paper fast as they do not charge anything. Run for the opportunity when and wherever you find. They help a lot to get connect with the people like you and share your new information among them. This opens the doors of academic success fast.

Published by: Neeraj Mishra

Publishing research paper in a free journal always motivates the researchers, scholars and students who are stepping through their academic journey. In some cases it seems hard to publish in free journal but the right approach at right time will help you to easy go with paper publication. In this blog you will understand what free engineering journals are and how they help you to publish papers easily.

Submit Your Paper  / Check Publication Charges

What are Free Engineering Journals?

The journals cover the branches of engineering as, civil, mechanical, quantum, chemical, industrial and software engineering and more, covering its different subjects as well. They are generally funded by govt. institutes, research institutes, universities or colleges. With these journals scholars can easily publish papers for free.

How These Journals Help you to Publish Papers?

When you are going to write for the research paper, do have clarity that it should be written with honesty and not copied, this protects you to avoid plagiarism and makes your research genuine. Write the paper in a standardized format which qualifies the basic standards of journal paper publication. Submit the paper in the journal which gives free submission access.
Free does not mean zero charges, there can be some hidden fee. Always check in the official site of the journals to assure charges or free because every journal which promise free publication is not always true. Check for the following traits in a free engineering journal;

• Publishes engineering field related papers
• Open access and gives free reading facility of already published articles
• Fast publication capacity with double blind review process
• Free and do not charge a high amount
• Provides paper submission guidelines and transparent payment process (when charges)
• Has high impact factor, more citations
• Give ISSN and DOI following journal policies.
• Digital publication certificate
• Better number of audience and known world wide

Give your academic journey a new direction, publishing your paper in a free journal but this requires a well written and formatted research paper, which proves its reliability among the disciplines as it is not easy to get easily publish research work for free.

Published by: Neeraj Mishra

Authors: Rohit Sunil Khedkara, Sharad Dhanvijay

Abstract: The escalating atmospheric CO₂ concentration and its contribution to global climate change have driven intensive research into carbon capture technologies. Ionic liquids (ILs) have emerged as promising alternatives to conventional amine-based absorbents, offering unique advantages including negligible vapor pressure, exceptional thermal stability, and tunable physicochemical properties through rational cation-anion design. This comprehensive review examines the full spectrum of ionic liquid applications in CO₂ capture, from fundamental absorption mechanisms to process-scale implementations. Physical absorption in conventional ILs, chemisorption in task-specific ILs incorporating amine, carboxylate, and amino acid functionalities, and IL-based mixed absorbents are systematically analyzed. Structure-property relationships governing CO₂ solubility—including the influence of cation alkyl chain length, anion basicity, and functional group incorporation—are critically evaluated against experimental and computational data. Supported ionic liquid membranes (SILMs) and ionic liquid-based mixed matrix membranes for CO₂ separation are reviewed, highlighting permeability-selectivity trade-offs and stability considerations. Process configurations including IL-based absorption-desorption cycles, membrane contactors, and hybrid systems are assessed for energy consumption and economic viability. Recent advances in computational screening, machine learning-guided IL design, and process intensification are presented. Key challenges including high viscosity, long-term stability under operating conditions, absorbent regeneration energy, and scale-up economics are addressed. Finally, future directions toward industrial implementation are discussed, emphasizing the integration of ILs with renewable energy sources and the development of sustainable, cost-effective capture technologies.

DOI: https://doi.org/10.5281/zenodo.19050263

 

Published by: vikaspatanker

Authors: Won Kuk Jin, Choe Jin Hyok

Abstract: Window design is a critical factor significantly influencing building aesthetics, daylighting performance, visual comfort, and energy consumption. Conventional energy-saving strategies often rely on reducing window area, which negatively impacts architectural aesthetics and user satisfaction. This study proposes a novel form-oriented design approach that enhances energy efficiency while maintaining the window area. Four window geometries—flat, polygonal, protruded, and curved—were compared under identical area and material conditions. Key performance indicators included U-value, Solar Heat Gain Coefficient (SHGC), cooling and heating loads, and daylighting performance. The analysis revealed that curved windows achieved the highest cooling performance with an 18.2% reduction in cooling load but exhibited a significant drawback with an 8.2% increase in heating load, indicating substantial winter heat loss. Protruded windows showed a minimal cooling load reduction of only 0.3% and a 3.6% increase in heating load. Polygonal windows demonstrated the most balanced performance, with a 7.1% reduction in cooling load and a 3.8% increase in heating load. These results suggest that in a cold climate like Pyongyang, winter heating performance has a greater impact on annual energy consumption than summer cooling performance, implying that window form selection should not be based solely on summer performance.

DOI: https://doi.org/10.5281/zenodo.19049364

Published by: vikaspatanker

Authors: Devansh Indrodiya, Shivangi Patel

Abstract: The integration of Large Language Models (LLMs) into modern search engines has significantly transformed digital discoverability, shifting search behavior from deterministic webpage ranking to probabilistic entity citation within AI-generated responses. Unlike traditional search engines that present ordered lists of hyperlinks, generative search systems synthesize contextual answers and selectively cite businesses based on semantic relevance, trust signals, review sentiment, and inferred user intent. This transformation challenges conventional Search Engine Optimization (SEO) strategies that were originally designed to optimize positional ranking rather than inclusion within generative responses. This paper introduces Generative Engine Optimization (GEO), a geospatial artificial intelligence framework designed to model, measure, and improve business visibility in generative search environments. The proposed framework integrates geospatial analysis, semantic entity recognition, and machine learning–based prediction models to evaluate discoverability within AI-generated responses. A monitoring system called GeoRank360 is developed to track business citations across multiple generative platforms and compute a unified metric termed the Generative Visibility Score (GVS), which incorporates citation frequency, semantic prominence, sentiment strength, entity consistency, and temporal stability. An empirical evaluation conducted across 100 local businesses, five generative search platforms, 500 query variations, and over 4,000 geo-grid coordinates reveals spatial visibility volatility ranging from 35% to 60%, substantially higher than fluctuations observed in traditional search rankings. Predictive modeling achieves up to 87.1% accuracy in forecasting generative citation outcomes. The results indicate that semantic relevance exerts greater influence than geographic proximity in determining visibility within generative search responses. The proposed GEO framework establishes a foundation for future research in generative search visibility modeling, semantic ranking analysis, and AI-driven local discovery systems.

Published by: vikaspatanker