Authors: Lattupalli Neelaveni, V.E.S.Mahendra Kumar

Abstract: The layout planning is a part of urban development it includes planning of residential houses, commercial complexes, service roads, primary health centers, school…& other amenities sewerage system for whole layout (includes treatment, sewer line, storm water drains), water distribution system. This project includes design& estimation of residential building in plot of layout planned. Designing involves identifying the loads which act upon a structure and the forces and stresses which arise within that structure due to those loads, perform analysis to get moments and shear forces on different elements of the structure and then design the structure for ultimate loads and moments. The loads can be self-weight of the structures, other dead loads, live loads, moving (wheel) loads, wind load, earthquake load, load from temperature change etc. Estimation includes finding the quantities of materials required for the construction of the structure and requirements of labor etc., finally determining the overall cost of the structure before execution of work by using Auto cad. Structural engineers are facing the challenge of striving for the most efficient and economical design with accuracy in solution, while ensuring that the final design of a building must be serviceable for its intended function over its design lifetime. This project attempts to understand the structural behavior of various components in the multi-storied building. Analysis, designing and estimation of multi-storied building has been taken up for Basement+G+2 Building, thereby depending on the suitability of plan, layout of beams and positions of columns are fixed. Dead loads are calculated based on material properties and live loads are considered according to the code IS875-part 2, footings are designed based on safe bearing capacity of soil. For the design of columns and beams frame analysis is done by limit state method to know the moments they are acted upon. Slab designing is done depending upon the type of slab (one way or two way), end conditions and the loading. From the slabs the loads are transferred to the beams, thereafter the loads from the beams are taken up by the columns and then to footing finally the section is checked for the components manually and for the post analysis of structure, maximum shear force, bending moment and maximum story displacement are computed. The quantitative estimation has been worked out. All the drafting was done using Auto cad.

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Published by: vikaspatanker

Authors: Chilaka Vijay, V.E.S.Mahendra Kumar

Abstract: Concrete has occupied an important place in the construction industry in the past few decades and it is used widely in all types of constructions ranging from small buildings to large infrastructural dams or reservoirs. Cement is a major ingredient of concrete. The cost of cement is increasing day by day due to its limited availability and large demand. At the same time global warming is increasing day by day. Manufacturing of cement releases carbon dioxide. In the present study an attempt has been made on concrete and an experimental investigation on the concrete by replacing cement with FLYASH and GGBS to decrease the usage of cement as well as emission of carbon dioxide. Experimental studies were performed on plain cement concrete and replacement of cement with Fly ash and GGBS was done. In this study the concrete mix was prepared by using fly ash, GGBS, sodium silicate, sodium hydroxide. A comparative analysis has been carried out for concrete to the Geo polymer concrete in relation to their compressive strength, workability, tests on aggregate. The Geo- polymer concrete is an innovative and eco-friendly in construction. To reduce carbon dioxide emission, we are making geo-polymer concrete. The concrete made with fly ash (50%) and GGBS (50%) performed well in term of compressive strength, shows higher performance at the age of 7,14,28 days than conventional concrete. slump cone, compaction factor test was conducted to find the workability of Geo-polymer concrete and normal concrete. And test conducted on aggregate such as crushing strength, abrasion test, impact test.

 

 

Published by: vikaspatanker

Authors: Joselin Mercy J, Rithu Kumari R, Dr. K. Geetha

Abstract: Traffic congestion has become a serious issue in rapidly growing cities, causing delays, increased fuel usage, and environmental damage. Traditional traffic systems rely on fixed signals and limited data, making them ineffective in handling real-time traffic variations. To overcome these limitations, this study introduces a smart traffic prediction system that combines Artificial Intelligence (AI) and the Internet of Things (IoT). The system gathers real-time data from devices such as traffic cameras, GPS trackers, and roadside sensors. This data is then analyzed using machine learning models, especially Long Short-Term Memory (LSTM), to predict future traffic conditions. The goal of this system is to improve traffic flow, reduce congestion, and support better decision-making for traffic authorities. With the help of cloud computing, the system can efficiently handle large amounts of data. Experimental results show that this approach performs better than traditional methods by improving prediction accuracy and reducing delays. Overall, this system contributes to smarter cities and better quality of life.

DOI: https://doi.org/10.5281/zenodo.19064146

Published by: vikaspatanker

Authors: Chinta Lakshmi Prasanna Kumar, Dr.K.Naga Sreenivasa Rao

Abstract: This paper presents a detailed laboratory-based experimental investigation on determining the optimum replacement levels of Fly Ash and Ground Granulated Blast Furnace Slag (GGBS) as supplementary cementitious materials in concrete. Ordinary Portland Cement (OPC) was partially replaced with GGBS at levels of 5%, 6%, 7%, 8%, 9%, and 10%, while Fly Ash was incorporated at replacement levels of 20%, 40%, and 60% of the total binder content. A constant water-to-cementitious materials ratio of 0.45 was maintained for all concrete mixes to ensure uniformity and comparability of results. The study was conducted on M25 grade concrete, designed with a mix proportion of 1:1.36:2.71.

 

 

Published by: vikaspatanker

Authors: Dr. Bayomock Linwa André Claude, Mr. Bakayoko Moussa

Abstract: This project proposes an innovative architecture that aims to ensure inter-mobile network financial transactions inside a specific country or between different countries. The architecture is a micro-service oriented. The architecture uses infrastructure as mobile server, gateways, that ensure interoperability, transparency and secure transactions between 2 separate mobile operators. Web technologies have been used to implement the solution. The architecture uses foundation principles of an open, efficient and inclusive financial ecosystem.

DOI: https://doi.org/10.5281/zenodo.19063263

Published by: vikaspatanker

Authors: Mr. Dr.M.Veerabhadra Rao, Munasa Satya Bhaskar

Abstract: The increasing use of social media platforms has created vast amounts of user-generated textual data that reflect personal emotions, thoughts, and behavioural patterns. These digital footprints provide valuable insights into an individual’s psychological state and can be leveraged for early detection of mental health conditions. However, traditional mental health assessment methods rely heavily on clinical interviews and self-reported questionnaires, which may not always provide timely or scalable solutions. This study proposes an intelligent hybrid machine learning framework for early mental health risk prediction using social media text analytics. The system integrates conventional machine learning models and deep learning architectures to perform multiclass classification of mental health conditions such as anxiety, depression, stress, and other psychological states. The framework incorporates comprehensive text preprocessing techniques, including cleaning, tokenization, stop-word removal, and feature extraction using advanced vectorization methods. Multiple classifiers such as Support Vector Machines (SVM), Random Forest, Logistic Regression, XGBoost, and a hybrid BiLSTM-CNN deep learning model are implemented and evaluated. To enhance performance, the proposed system applies hyperparameter optimization and dynamic model selection strategies. Experimental results demonstrate that the hybrid framework achieves high predictive accuracy and balanced performance across precision, recall, and F1-score metrics. The system provides a scalable and automated approach for mental health analysis, offering potential support for early intervention and preventive healthcare strategies.

DOI: https://doi.org/10.5281/zenodo.19063137

Published by: vikaspatanker

Authors: Challa Prasad, Sk.Abdulkareem

Abstract: Concrete is the most widely used construction material in the world, but its inherent brittleness and low tensile strength often limit its performance in structural applications. To overcome these limitations, the addition of fibres into the concrete mix has emerged as an effective technique to improve mechanical properties such as tensile strength, ductility, toughness, and impact resistance. This study investigates the mechanical behaviour of hybrid fibre-reinforced concrete (HFRC) incorporating a combination of steel fibres and polypropylene fibres. Steel fibres are known for their high tensile strength and crack-bridging capacity, while polypropylene fibres enhance post-crack behaviour and resistance to plastic shrinkage cracking. The experimental program includes the preparation of various concrete mixes with different proportions of hybrid fibres, followed by testing for compressive strength, split tensile strength, and flexural strength. The results demonstrate that the synergistic effect of steel and polypropylene fibres significantly enhances the mechanical performance of concrete compared to conventional plain concrete and single-fibre mixes. The research highlights that an optimal hybrid fibre ratio exists, which maximizes strength and ductility without compromising workability. The study provides valuable insights for structural engineers and researchers aiming to improve the durability and performance of modern concrete structures.

 

 

Published by: vikaspatanker

Authors: Mr. G. Harsha Vardhan, Shaik Kareem Ahmed

Abstract: The rapid expansion of digital media platforms has significantly increased the spread of misinformation, posing serious threats to public opinion, political stability, and social harmony. The automated identification of fake news has therefore become a critical research challenge in the fields of machine learning and natural language processing. This paper presents an intelligent and robust fake news detection framework that leverages advanced textual feature extraction and ensemble learning techniques to improve classification performance. The proposed system incorporates comprehensive data preprocessing, including text normalization, stop-word removal, tokenization, and vectorization using TF-IDF representations. Multiple supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting are trained and evaluated using stratified cross-validation to ensure reliability and generalization. To enhance predictive accuracy and reduce model bias, an ensemble-based voting mechanism is employed. Performance evaluation is conducted using metrics including accuracy, precision, recall, F1-score, and ROC-AUC to address class imbalance and misclassification risks. Experimental results demonstrate that the ensemble framework achieves superior performance compared to individual classifiers, providing a scalable and dependable solution for real-time misinformation detection in digital environments. The proposed approach contributes toward building trustworthy information ecosystems through automated and explainable fake news classification.

DOI: https://doi.org/10.5281/zenodo.19062457

Published by: vikaspatanker

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