Authors: Sneha Sankeshwari, Santosh Gaikwad, Arshiya Khan, R.S. Deshpande

Abstract: Lung cancer is one of the leading causes of cancer-related mortality worldwide, primarily due to delayed diagnosis and limited access to timely screening. Early detection is essential for improving survival outcomes, yet conventional diagnostic techniques such as CT scans, X-rays, and biopsies are often expensive, time-consuming, and not readily available in all healthcare settings. This study explores the potential of machine learning (ML) techniques in facilitating early and accurate lung cancer prediction by leveraging structured patient data, including age, smoking history, environmental exposures, and family medical background. Various ML models—including Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines—are evaluated for their effectiveness in identifying high-risk individuals. Publicly available datasets, such as the UCI Lung Cancer Dataset, SEER database, and PLCO trial data, are utilized for training and validation. The study also addresses key challenges in ML-based diagnosis, including data imbalance, feature selection, and model interpretability. Additionally, future research directions are highlighted, particularly the integration of multi-modal data and the deployment of interpretable AI solutions in clinical practice. The findings underscore the promise of ML in making lung cancer detection more accessible, efficient, and cost-effective, ultimately contributing to reduced mortality rates.

 

 

Published by: vikaspatanker

Authors: Raghu V Kaspa

 

Abstract: High Performing Organizations (HPOs) consistently outperform their peers in metrics such as innovation, agility, financial results, and employee engagement. This paper explores the critical attributes that characterize HPOs and applies these attributes to Tesla, Inc., as a case study. Through an analytical lens grounded in organizational theory, performance frameworks, and empirical evidence, Tesla’s rise as a global automotive and energy leader is examined to identify the drivers of its high performance.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.154

 

Published by: vikaspatanker

Authors: Rushali Manwatkar, Saloni Jaiswal, Professor Yogesh Patidar

Abstract: Image retrieval is a poor stepchild to other forms of information retrieval (IR). Image retrieval has been one of the most interesting and research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Colour, shape and texture features are important properties in content-based image retrieval systems. In this paper, we have mentioned detailed classification of CBIR system. We have defined different techniques as well as the combinations of them to improve the performance. We have also defined the effect of different matching techniques on the retrieval process. Most content-based image retrievals (CBIR) use color as image features. However, image retrieval using color features often gives disappointing results because in many cases, images with similar colors do not have similar content. Color methods incorporating spatial information have been proposed to solve this problem, however, these methods often result in very high dimensions of features which drastically slow down the retrieval speed. In this paper, a method combining color, shape and texture features of image is proposed to improve the retrieval performance. Given a query, images in the database are firstly ranked using color features. Then the top ranked images are re-ranked according to their texture features. Results show the second process improves retrieval performance significantly.

 

 

Published by: vikaspatanker

Authors: LOKESWARAN. V, SHANMUGAVADIVU. M

Abstract: Protease enzymes play an important role in many biological processes, including protein digestion, cell signals and protective mechanisms. In this study, the isolation, refining and partial characteristics of the protease enzyme of Guava (Psidium Guajava) have been studied. Dry guava leaves are homogeneous and extracted by raw enzymes using phosphate stamps. The activity of the protease has been determined by the digestion of casein and raw enzyme extract refined by precipitating in ammonium sulfate. The specific activity of pure protease enzyme is significantly higher than the rough extract. The enzyme is characterized by its pH, its optimal temperature and stability, showing the maximum operation at pH 4 and 60° C. In addition, the molecular weight of the protease enzyme is about 135 kda. In addition, the ability to decrease protein enzyme shows its ability to apply in soft meat, hydrolysed protein in food processing and remove points in laundry detergent. This study emphasized that the promising potential of guava panels is a profitable and environmentally friendly biological substance for industrial applications, especially in the fields of food and detergents. Add in -Depth on its complete enzyme records and its industrial scale is reasonable

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Dr.Pranesh Kulkarni ., Assistant Professor Department of Mathematics

Abstract: Mathematics is a fundamental component of Data Science, providing the theoretical foundations for many data analysis and Machine learning techniques. A breakdown of the fundamental math field required for data Science, Linear Algebra, Calculus, and Probability Theory. Through mathematics we can learn data analysis and visualization in this we learn plotting, charting and data storytelling.in this Article we discussed structuring and designing of mathematics in data science this provides a Comprehensive framework for understanding the mathematical foundations. Data analysis and visualizations, machine learning And modeling, and mathematical techniques used in data science. Being a data scientist is more than just using plug-and- play machine learning packages. Educators have to understand what the algorithm is actually doing first and foremost and know when and why to use it. The process to learn what the algorithms are doing is by studying the underlying mathematics. We know that Geometry and graph theory form essential pillars of data science, it providing tools to model, analyze, and visualize complex relationship. These mathematical concepts enable data scientists to efficiently uncover patterns, optimize systems, and efficiently represent intricate datasets. Now, I know “Big Data” and “Hadoop” have become a bit of a big deal in the data world and are being thrown around like a cool fad, but it feels like a lot of people still don’t really understand the concept behind it. In this article I’ve covered the why and what of Open-source software how does it all actually work? Data is essential for ML- enabled systems. Poor data will result in inaccurate predictions, which are referred to in the ML context as “garbage in, garbage out”. Hence, ML requires high-quality input data. From the viewpoint of RE, it is clear that data constitutes a new type of requirements Based on the Data Quality model defined in the standard ISO/IEC 25012, we elaborate on the data Perspective.

DOI: http://doi.org/10.5281/zenodo.15833884

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Authors: Dr. Rajul Misra, Mr. Saurabh Saxena, Abhishek Singh,, Tushar Chauhan, Anit kumar

Abstract: This report presents a comprehensive study on Wireless Power Transmission (WPT), a groundbreaking technology that facilitates the transfer of electrical energy without the need for physical connectors or wires. The project explores various methodologies, including inductive coupling, resonant inductive coupling, and advanced techniques such as beam forming and UV-assisted wireless charging. The primary objective is to design an efficient WPT system capable of delivering power over varying distances while addressing challenges related to efficiency, range limitations, and safety standards. Through experimental evaluations, the project demonstrates that resonant inductive coupling enhances energy transfer efficiency and extends operational range compared to traditional methods. Additionally, the integration of innovative techniques like quasi-static cavity resonance allows for simultaneous charging of multiple devices. The findings indicate that while WPT systems hold significant promise for applications in consumer electronics, electric vehicles, and medical devices, ongoing research is essential to overcome existing challenges and facilitate widespread adoption. This study contributes valuable insights into the development of wireless energy transmission technologies and their potential impact on various industries

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Yash Malsuare, Aryan Purohit, Isha syed, Dr. Maheshwari Birada

Abstract: This paper presents SuperAgent, a novel multi-agent AI framework designed to autonomously handle complex, real-world tasks through intelligent collaboration among dynamic language agents. As the capabilities of large language models (LLMs) continue to advance, there remains a gap in practical deployment frameworks that can translate user intentions into real-world actions with minimal supervision, explainable reasoning, and reliable execution. SuperAgent+ bridges this gap by combining prompt-driven agent generation, transparent multi-step task planning, and API-integrated tool use in a modular architecture that supports human oversight and customization. At the core of SuperAgent+ lies a flexible orchestration engine that dynamically instantiates and manages specialized agents for subtasks such as information retrieval, summarization, decision-making, scheduling, verification, and real-world communication. Users can design and visualize workflows using a drag-and-drop interface, enabling domain experts and non-technical users alike to create autonomous workflows without writing code. The system further integrates a memory layer for context retention, a reasoning logger for traceability, and real-world tool access (e.g., calendars, calls, databases) for execution beyond the digital domain. We evaluate SuperAgent across a variety of tasks such as academic research assistance, enterprise automation, personal productivity planning, and multi-modal content generation. Our results demonstrate improvements in task completion rates, reasoning transparency, and adaptability compared to baseline single-agent and static pipeline systems. This research lays the foundation for future work on fully autonomous AI ecosystems capable of safe, reliable, and cooperative task execution across domains. Furthermore, this research integrates a modular plug-and-play architecture, enabling extensibility for future agents, tools, or models (e.g., vision, audio, or robotic modules). Experimental evaluations indicate substantial gains in task efficiency, traceability, scalability, and user satisfaction, especially in domains such as software development, research summarization, data analysis, and automated reporting. (Stein, Helge Sören and J. Gregoire).

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Nikhil Chavan, Dr. Rachna Chavan,, Dr. A. A. Khan, Dr. R. S. Deshpande

Abstract: – Water scarcity is a growing global challenge, making it essential to monitor and optimize water consumption effec- tively. This paper presents an AI-based Smart Water Consump- tion Monitoring System that leverages machine learning and IoT technologies to enhance water management. The system utilizes real-time sensor data, applies predictive algorithms, and generates insights to optimize water usage, reduce waste, and detect anomalies. The proposed system aims to encourage sustainable water consumption practices and prevent potential water-related crises.

Published by: vikaspatanker

Authors: Isha Vyas, Professor Amit Thakur

Abstract: Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. Variety of sensing capabilities results in profusion of application areas. However, the characteristics of wireless sensor networks require more effective methods for data forwarding and processing. In WSN, the sensor nodes have a limited transmission range, and their processing and storage capabilities as well as their energy resources are also limited. Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop communication under these conditions. In this Review work, we give a survey of routing protocols for Wireless Sensor Network and compare their strengths and limitations.

 

 

Published by: vikaspatanker

Authors: Deepti Tripathi, Professor Amit Thakur

Abstract: The Wireless sensor networks (WSN) are becoming popular as an emergent requirement for manhood. Although, these networks are developing vary rapidly but, they can be used in approximately all aspects of the life. A thorough analysis of existing protocols was conducted to understand problems of WSN and few evaluation tables have been provided for the review summary of the performance of the protocols according to parameters such that latency, scalability, transmission type, network traffic and energy perception. Components of the WSN have been discussed in detail. Issues and challenges of WSN were discussed. Different energy harvesting resources and technologies have been analyzed.

 

 

Published by: vikaspatanker