Authors: K. Perarasu

Abstract: The stock market is a crucial component of the financial system, playing a significant role in economic development and wealth creation. Traditional financial theories assume that investors behave rationally and make decisions based on complete information. However, practical market observations reveal that investor behavior is often influenced by psychological, emotional, and social factors. This study aims to examine stock market trends and analyze how investor behavior impacts market movements. The research adopts an empirical approach using both primary and secondary data. Primary data is collected through structured questionnaires administered to individual investors, while secondary data is obtained from stock market indices, financial reports, and published research studies. Statistical tools such as percentage analysis, correlation, and graphical interpretation are employed to analyze the data. The findings reveal that investors frequently exhibit behavioral biases such as herd behavior, overconfidence, loss aversion, and risk aversion. Market trends show significant volatility during periods of economic uncertainty, indicating emotionally driven investment decisions. The study concludes that investor behavior plays a vital role in shaping stock market trends and that incorporating behavioral finance concepts can enhance investment decision-making and market stability.

DOI:

 

 

Published by: vikaspatanker

Authors: Chinmay Chouhan, Srashti Thakur

Abstract: Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

DOI:

 

 

Published by: vikaspatanker

Authors: Kartik Dadore, Jitendra Chouhan

 

Abstract: In India, the road traffic volume has increased manifolds during the post-independence period. The traffic axle loading may also in many cases be much heavier than the specified limit. As a result of which, the existing road network has been subjected to severe deterioration leading to premature failure of the pavements. In such a scenario, development of the effective pavement management strategies would furnish useful information to ensure the compatible and cost- effective decisions so as to keep the existing road network intact. The pavement deterioration models can prove to be an effective tool which can assist highway agencies to forecast economic and technical outcome of possible investment decisions regarding maintenance management of pavements. The optimum maintenance and rehabilitation strategies developed in this study would be useful in planning pavement maintenance strategies in a scientific manner and ensuring rational utilization of limited maintenance funds. Once this strategy for urban road network is implemented and made operational; this would serve as window to the other urban road network of different regions.

DOI:

 

Published by: vikaspatanker

Authors: Ms. Jyoti Ahlawat, Research Scholar, Dr. Banita, Associate Professor

Abstract: Leukemia is a life-threatening hematological malignancy that requires early and accurate diagnosis to improve patient outcomes. Manual examination of microscopic blood smear images is time-consuming, subjective, and highly dependent on expert pathologists. With recent advances in artificial intelligence, deep learning has emerged as a powerful tool for automated medical image analysis. The goal of this research paper is to develop a deep learning-based model that can accurately detect leukaemia from medical images, with a focus on optimizing the model’s performance using advanced techniques such as transfer learning, hyper parameter tuning, and regularization methods. Evaluation metrics such as accuracy, precision, recall, F1 score, and the ROC-AUC curve will be used to assess the model’s diagnostic ability. By building a robust and scalable deep learning model for leukaemia detection, this study aims to contribute to the growing body of research on AI-driven medical diagnostics and provide a practical tool to assist healthcare professionals in early leukaemia diagnosis.

DOI:

 

 

Published by: vikaspatanker

Authors: A.Swarna

Abstract: Self-reinforced composites (SRCs), also termed single-polymer composites, are engineered so that the reinforcing phase and matrix belong to the same polymer family. By eliminating chemical mismatch at the interface, SRCs typically show improved interfacial integrity, low density, and high impact tolerance while remaining compatible with single-stream recycling. Recent work (2020–2025) has emphasized processing control, bio-based SRC platforms, and microstructure-driven property tailoring.”This review provides a comprehensive discussion of SRC fundamentals, fabrication strategies, structure–property relationships, environmental advantages, application sectors, and future research directions.

DOI:

 

 

Published by: vikaspatanker

Authors: Annu Singh, Basant Kumar Mishra

Abstract: Blood flow in human arteries is inherently pulsatile and exhibits non-Newtonian behavior, driven by the rhythmic cardiac cycle and influenced by shear-dependent viscosity arising from plasma and cellular interactions. This study investigates the magnetohydrodynamic (MHD) effects of a transverse magnetic field on pulsatile non-Newtonian blood flow, with particular emphasis on velocity distribution, wall shear stress (WSS), flow resistance, and hemodynamic responses in stenosed arteries. Blood is modeled as a Casson fluid, capturing shear-thinning and yield stress characteristics, while the transverse magnetic field generates a Lorentz force opposing flow. Governing momentum equations are formulated in cylindrical coordinates and solved using analytical techniques (Finite Hankel transforms) complemented by numerical simulations for pathological and pulsatile conditions. The analysis reveals that increasing the Hartmann number (Ha) significantly reduces centerline velocity, flattens velocity profiles, and decreases WSS, whereas higher Casson parameters (β) produce blunter, plug-like profiles with higher central velocity and lower boundary shear. Pulsatility, represented by the Womersley number (α), introduces phase-lagged oscillations, and stenosis severity amplifies local velocities and WSS, increasing flow resistance. Additionally, Joule heating due to induced currents modestly raises blood temperature, relevant for hyperthermia therapy. These findings have significant implications for MRI safety, magnetic drug targeting, and vascular disease management, providing quantitative insight into the interplay of magnetic fields, non-Newtonian rheology, and pulsatile hemodynamics in arteries.

DOI:

 

 

Published by: vikaspatanker

Authors: Preethi Wilson G, Gokulakrishnan R, Dhivya dhanasree S S, Sumanth BKM

Abstract: A virtual try-on system is an advanced AI-powered platform that allows users to visualize how clothing items would appear on their bodies without physically wearing them. These systems are transforming the way people shop online by offering a digital fitting room experience using computer vision, deep learning, and generative models. in recent years, the demand for online fashion experiences has increased, encouraging the development of systems like Style VTON, which not only allows users to try on clothes virtually but also supports multiple body poses and preserves personal identity and clothing details. By using a combination of input images (user photo, clothing image, and target pose), such systems generate a highly realistic image of the user wearing the desired outfit in a new posture

DOI: https://doi.org/10.5281/zenodo.18597231

Published by: vikaspatanker

Authors: Sarvesh Bhandari, Rohan Wakchaure, Aarya Mahajan, Gauri Gadakh, Vaibhav Bhokare

Abstract: In the competitive and rapidly changing business world of today, organizations make greater use of business analytics to inform smart, sustainable choices. This paper discusses how analytics tools and techniques can help an organization enhance operational efficiency, time its forecasts better, and embrace strategies that could ensure long-term sustainability. Integrating descriptive analytics with diagnostic, predictive, and prescriptive analytics helps turn raw data into actionable insights for businesses to drive efficient resource utilization, better customer understanding, and strategic planning. The role of modern technologies, such as machine learning, business intelligence systems, and real-time dashboards, has also been discussed in enhancing the data-driven decisioning process. It also investigates how the application of business analytics can result in environmental, social, and economic sustainability by minimizing waste, optimizing operations, and encouraging responsible business operations. The study points out that based on the literature review and practical applications, there is a strong need for analytical competencies and a data-driven culture within organizations. The conclusions highlight that leveraging business analytics is an important pathway not only to attaining competitive advantage but also to sustainable and resilient business growth. This paper also emphasizes the importance of integrating sustainability goals into the analytical models that support balanced and responsible decision making. With the pressure by stakeholders, regulators, and consumers increasing in sustainability matters, being able to link performance metrics together with environmental and social indicators increasingly becomes a priority competency for business. Business analytics lets organizations assess the effects of their long-term decisions, measure sustainability performance, and helps organizations make decisions that not only benefit them but also align with global standards like ESG frameworks. By highlighting practical examples of emerging trends, the paper shows how analytics-driven insights empower organizations to innovate, reduce risks, and build sustainable value for all.

DOI: https://doi.org/10.5281/zenodo.18597175

Published by: vikaspatanker

Authors: Vishlesha Anil Habib, Vidya Gorakh Jagtap, Shrawani Ravindra Gaikwad, Nagraj Yashwant Kherud, Vaijayanti Pradip Kolhe

Abstract: The exponential growth of online platforms has enabled rapid dissemination of information, but it has also facilitated the widespread propagation of fake news. Fake news has negatively impacted political stability, public health, social harmony, and digital trust. This paper presents a comprehensive study and implementation of machine learning (ML) and Natural Language Processing (NLP)-based techniques for detecting fake news. The proposed system uses advanced text preprocessing, TF-IDF feature extraction, and multiple ML algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Naïve Bayes. Experimental results show that SVM achieves the highest accuracy of 94.8%, outperforming other models. This work demonstrates that combining linguistic features and machine learning provides a scalable and reliable approach to combat misinformation. Future enhancements include using transformer-based deep learning models and multilingual datasets

DOI: https://doi.org/10.5281/zenodo.18596939

Published by: vikaspatanker

Authors: Sachin Kumar

 

Abstract: The rapid evolution of wireless communication technologies has led to the emergence of sixth-generation (6G) networks, which aim to support ultra-low latency, massive connectivity, and intelligent network automation. One of the critical challenges in 6G is efficient mobility management due to highly dynamic user behavior, ultra-dense networks, and heterogeneous access technologies. Traditional mobility management schemes rely on reactive handover mechanisms that often result in increased latency, packet loss, and signaling overhead. To address these limitations, predictive mobility management has gained significant attention. This paper proposes the use of Long Short-Term Memory (LSTM) networks, a type of deep learning model well-suited for sequential data, to predict user mobility patterns in 6G networks. By leveraging historical mobility data, the LSTM-based approach enables proactive handover decisions, improved resource allocation, and enhanced Quality of Service (QoS). The paper discusses the architecture, working principle, advantages, and applicability of LSTM-based predictive mobility management in 6G environments, highlighting its potential to enable intelligent and autonomous network operations

DOI: https://doi.org/10.5281/zenodo.18596515

 

Published by: vikaspatanker