International Journal of Engineering and Science Research
The Cornerstone Of Iob-Powered Strategies For Digital Transformation Success
When the behaviour of human beings impacts how companies use data in usage-based strategies and perform production for personalisation, which leads to the term coined as the “Internet of Behaviour”. An overview of client behaviours can be improved by including information on online usage, network-connected device usage, and behaviour analytics. To drive technological implementations with heaps of data, businesses now require IoB knowledge. This study examines the potential of data-driven creativity to digitally revolutionise the Internet of Business and helps us assess how data types and volumes in the IoB relate to digital transformation outcomes. This requires close examination. According to IoB, organisations that effectively manage and achieve the diversity and amount of data maximise their value. The better the data integration and analytical capacity within a firm, the more it can leverage IoB. The research examines interoperability and the balance between privacy and data quality, with immediate implications for data strategies. A small survey also includes comprehensive analysis and practical recommendations for IoB-based digital businesses. The findings on strategic data help consultants using the IoB approach to realism the benefits of digital transformation by balancing data heterogeneity and volume.
Internet of Behaviour, Digital Transformation, Strategic Data Management, IoB Implementation, Digital Transformation Success, Comparative Data Strategies
Ai-Driven Business Analytics For Financial Risk Mitigation And Strategic Human Resource Management
The convergence of artificial intelligence (AI) and business analytics has fundamentally transformed organizational approaches to financial risk mitigation and strategic human resource management (HRM) between 2023 and 2025. This study examines how machine learning algorithms, predictive modeling, and data-driven decision-making frameworks enhance credit risk assessment accuracy while simultaneously optimizing workforce planning and talent retention strategies. In financial services, ensemble methods such as XGBoost and Random Forest have demonstrated superior predictive performance over traditional logistic regression models, achieving ROC-AUC scores exceeding 0.91 in commercial loan default prediction while addressing class imbalance through synthetic minority oversampling techniques. Concurrently, AI-powered HR analytics systems have enabled organizations to predict employee turnover with up to 87% accuracy, reducing voluntary attrition by approximately 15% through proactive intervention strategies. The research integrates two traditionally siloed domains—financial risk management and human capital analytics—through a unified framework of predictive intelligence. Empirical evidence indicates that financial institutions implementing advanced analytics workbenches experienced corporate and commercial revenue increases exceeding 20% over three-year periods, while organizations deploying comprehensive HR analytics reported 31% improvements in internal mobility success rates and 23% enhancements in recruitment quality. The study employs comparative analysis of machine learning methodologies across both domains, evaluating algorithmic performance, interpretability through explainable AI (XAI) techniques such as SHAP values, and regulatory compliance requirements. Key findings reveal that tree-based ensemble models consistently outperform traditional statistical approaches in handling non-linear relationships and high-dimensional datasets characteristic of both credit risk and employee attrition prediction. However, the research identifies critical challenges including algorithmic bias, model interpretability limitations, data privacy concerns, and the need for robust governance frameworks. The study contributes to existing literature by demonstrating how integrated analytics platforms can align workforce strategies with financial outcomes, creating synergistic effects that enhance organizational resilience and competitive positioning. Recommendations emphasize the necessity of ethical AI implementation, continuous model validation, and cross-functional collaboration between risk management and HR analytics teams to maximize return on investment while ensuring transparency and fairness in automated decision-making systems.
Artificial intelligence, Credit risk prediction, Strategic human resource management, Predictive analytics, Machine learning, Employee retention,
A Systematic Literature Review and Empirical Study of AI-Supported Marketing Decision-Making in Indian Digital Firms.
In the bustling digital marketplace of modern India, where over 950 million internet users navigate a complex web of brand interactions, a quiet revolution is reshaping how marketing decisions are made. Artificial intelligence, once confined to science fiction, now sits beside human marketers in digital agencies across Mumbai, Bangalore, and Delhi, collaboratively steering campaigns that reach billions. Yet this partnership remains poorly understood, particularly in the context of Indian digital agencies where cultural diversity, linguistic complexity, and rapid technological change create unique challenges. While global studies celebrate AI's potential, they overlook how human marketers and intelligent machines collaborate in emerging economies, leaving critical gaps in both theory and practice. This study addresses that void through a systematic literature review and empirical analysis of Human-AI collaborative marketing intelligence, focusing specifically on AI-powered chatbot implementations from January 2023 to January 2026. Drawing upon complementary theory and Huang-Rust's collaborative framework, we analyse secondary data from NASSCOM's AI Adoption Index 2.0, IAMAI digital economy reports, and survey responses from 147 Indian digital marketing agencies. Our findings reveal that 87% of Indian enterprises operate at early stages of AI maturity, with digital agencies showing a 66% adoption rate—significantly higher than global averages. Agencies implementing structured Human-AI collaborative chatbot systems report 40% higher user engagement and 25% reduced operational costs compared to siloed approaches. However, inconsistent leadership commitment and ad-hoc implementation strategies persist as major barriers. This research contributes to marketing theory by extending collaborative intelligence frameworks to the Indian context and provides practical guidelines for optimising Human-AI partnerships in digital agency settings.
Human-AI collaboration, marketing intelligence, chatbots, digital agencies, India, complementary theory
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Edge Data Deduplication With Robust Optimization Amidst Uncertainties
The rapid expansion of data generated by mobile and Internet-of-Things (IoT) devices has placed immense pressure on traditional cloud infrastructures, particularly in terms of latency and storage efficiency. Mobile Edge Computing (MEC) has emerged as a promising paradigm by relocating computation and storage resources closer to end users. However, the limited capacity of edge servers introduces new challenges in data management. This paper presents a robust optimization-based data deduplication framework tailored for MEC environments. The proposed system incorporates uncertainty-aware mechanisms to handle dynamic factors such as user mobility, fluctuating workloads, and edge server failures. Two algorithms—uEDDE-C and uEDDE-A—are introduced to balance accuracy and computational efficiency. Experimental insights indicate that the framework significantly improves storage utilization, reduces latency, and enhances system reliability under uncertain conditions.
Mobile Edge Computing (MEC), Data Deduplication, Robust Optimization, Uncertainty Modeling, Edge Computing, Storage Optimization, Low-Latency Systems, Distributed Systems, Resource Management, Algorithm Design.
P.Sai Kiran Rao,R.Aditya Reddy,V.Bhavani
In Today’s World Robotics is a fast-growing and very interesting field. The concept of Robotics is now used in every sector whether it is in manufacturing industry, medical, transport, etc. The project is designed to build an obstacle avoidance robotic vehicle using ultrasonic sensors for its movement. Arduino is used to achieve the desired operation. A Robot is a machine that can perform task automatically or with Guidance. The Project proposes Robotic vehicle that has an intelligence built in it such that it directs itself whenever an obstacle comes in its path. This Robotic vehicle is built, using Arduino. An Ultrasonic sensor is used to detect any obstacle ahead of it and sense a command to the Arduino. Depending on the input signal received, the Arduino redirects the robot to move in an alternate direction by activating the motors which are interfaced to it through a motor driver. At the same time it can control steering gear to realize the obstacle avoidance function. The Robot car uses front axle steering, rear wheel drive arrangement. Two drive tyres are driven by two DC Motors with gear reduction mechanisms.
Robotics, Obstacle Avoidance, Arduino, Ultrasonic Sensor, Autonomous Robot, Embedded Systems, Motor Driver, DC Motors, Sensor-Based Navigation, Mobile Robot, Collision Detection, Control Systems.
Sampeta Ankitha, Vattikoti Gnaneshwari, Mallela Neha
Dynamic Workflow Optimizer For Remote Teams
The Dynamic Workflow Optimizer is an AI-enabled system designed to enhance productivity, transparency, and coordination in remote team environments. Traditional tools used for distributed work often result in inefficient task tracking, imbalanced workloads, and limited accountability. To address these limitations, the proposed system integrates task management, communication analysis, performance monitoring, and time tracking into a unified platform. The system continuously gathers and analyzes data related to user activity, task progress, communication behavior, and working hours. Advanced techniques such as sentiment analysis, workload optimization, conflict detection, and deadline prediction are applied to generate actionable insights. It also monitors login duration and active work time to ensure accurate productivity assessment. An interactive dashboard presents real-time analytics, including workload distribution, team performance, and productivity trends. Additionally, the system provides intelligent recommendations for task reassignment and deadline adjustments, supporting proactive decision-making. Built using React.js, FastAPI, and MongoDB, the platform ensures scalability, performance, and secure access through JWT-based authentication. By transforming operational data into meaningful intelligence, the system minimizes manual effort, improves coordination, and supports efficient workflow management in modern organizations.
Artificial Intelligence (AI), Workflow Optimization, Remote Collaboration Systems, Task Scheduling and Allocation, Sentiment Analysis, Workload Balancing, Predictive Analytics, Performance Monitoring, Time Tracking Systems, Decision Support Systems (DSS), Human–Computer Interaction (HCI), Machine Learning.
M. Siddarth, N. Varsha, Tahura Tahniyat, Devansh Agarwal
Mapping Big Data's 5V Constraints To Machine Learning Architecture And Scalability
The speedily developing field of big data analytics uses machine learning to analyse large and diverse information to help companies make smart business decisions. Analytics of Big data is important for national intelligence, cybersecurity, biology, fraud detection, and medical informatics. The use of massive volumes of data powered the development of big data in the early 2000s. The term "Big Data" refers to data collections of high size, speed, or complexity, making normal processing methods unsatisfactory. Large datasets have important potential, and machine learning powers artificial intelligence to extract information to support informed decision-making. In this article, we discuss machine learning procedures, big data knowledges, and applications of machine learning. This article discusses noteworthy issues with applying machine learning on large datasets. This research will inspect the current advances and problems at this unique convergence, revealing the potential for transformation in big data processing using machine learning.
Big Data, Data Analytics, Machine Learning, Big Data Analysis, Big Data Coordination ML
Iob As A Catalyst For Customer-Centric Digital Transformation: Personalisation, Engagement, And Loyalty Enhancement
Digital transformation has reshaped the relationship between banks and their customers, spurred on by fresh, customer-centric approaches. This transformation is underpinned by the Internet of Behaviors (IoB), a paradigm that utilizes data to analyze and influence human conduct. The IoB is, in turn, reshaping how financial institutions customize their products, engage with customers, and build customer loyalty. Therefore, this study seeks to explore the IoB's potential to refine banking strategies and operational practices, with the aim of improving client service. This study aims to analyze the digital changes in banking, focusing on how clients interact, the use of personalized services, and the effectiveness of loyalty programs. The study focuses on the existing strategies and its flaws and seeks to find major data IOB opportunities to increase client satisfaction. This investigation employs a comprehensive qualitative and quantitative methodology to analyze the Internet of Behaviors (IoB) within the banking industry, rethinking existing strategies, client engagement, and loyalty initiatives, and enhancing personalized financial guidance. The investigation examines the impact of the Internet of Behaviors (IoB) on the banking industry, shifting from traditional, model-based client interactions to data-driven strategies that leverage consumer insights in real time. The IoB offers the banking sector a pathway for digital transformation, enabling the enhancement of relationships, the augmentation of client value, and the adoption of a more customer-focused methodology. This research contributes to both academic understanding and practical use, thereby establishing a foundation for new ways of interacting with clients in the banking sector.
IoB (Internet of Behaviours), Customer-Centric Digital Transformation, Personalization, Customer Expectations, Control and Autonomy, Long-term Impact, Banking and Finance, Personalization
Attention And Reasoning From Experts In Smart Manufacturing With Digital Twin Implementations
When it is well known that expert attention is a way to separate each attention head into its own expert in a Mixture-of-Experts design, we understand that, inside the implementation of the digital twin, we need to examine how expert attention and reasoning engagement can enable operative Human-Machine Partnership in Industry 6.0 environments in the near future. We focused on analysing how user-centric design elements moderate these relationships, based on data from the most successful factory projects. A multiple-case study approach was used to examine data from Siemens, GE, and Bosch, three major industry players, which were publicly available. The study used a structured qualitative review of performance measures, system designs, and reported results from Digital Twin applications in the Manufacturing sectors. Three key patterns appear throughout the study. First, expert attention requires intelligent information filtering rather than increased data availability, as the latter would only take up space. Second, the reasoning involvement depends on AI answers that are clear, build trust, and can allow control. Third, user-centric design increases the cognitive effects when matched to users’ expertise levels. All three companies in the study demonstrated double-digit improvements in efficiency, accuracy, and response times over cognitive-focused Digital Twin designs. Throughout the study, we found that companies should invest in explainable AI, match interface complexity to user expertise, and design for learning rather than performance to better implement Digital Twins. They should also prioritise attention support through intelligent filtering, reasoning support through transparent clarifications, and user-centric revision. This study advances Industry 6.0 from a conceptual vision to a practical reality, providing the first systematic analysis of the mechanisms of cognitive engagement in operational Digital Twin systems and offering a framework for experts and a basis for future research.
Industry 6.0; Digital Twin; Human-Machine Collaboration; Cognitive Engagement; Expert Attention; Reasoning; User-Centric Design; Case Study; Siemens; GE; Bosch.
Digital Twin Technologies In Transition: A PRISMA-Guided Analysis Of Architectures, Applications
Digital twin technology refers to the use of synchronised digital replication for the modelling, management, and enhancement of large-scale cyber-physical systems. As such, digital twin systems have been simplified (in terms of integration) using artificial intelligence (AI), cloud computing, and data-driven analytics. Unfortunately, rapid technological progress has led to many unresolved issues regarding the technology, methods, and structures of digital twins. A systematic literature review of the relevant digital twin literature (2023-2026), conducted using the PRISMA methodology, served as the basis for understanding advances through analytical overviews of the findings. After a thorough review of all relevant peer-reviewed research, including quality assessments and a comparison of articles by their main goals, we found that digital twin systems, which were previously considered fixed, are now being replaced by AI-driven analytics and cloud-edge computing systems. These new systems are modular, layered, and/or distributed. Such systems, which currently exist in the healthcare, energy, digital infrastructure and industrial sectors, each have their own rules for manufacture and operation. Research into AI-augmented digital twins is underway to determine the extent to which they can explain, verify, synchronise, or enable cross-platform interoperability and the management of large datasets. While the results of these studies are like those of earlier research, it's important for researchers to also consider standard design frameworks, useful analytics, and strict measurement methods. Moreover, the development of a modern, technically sound synthesis has greatly improved the creation, testing, and use of advanced digital twins.
System Architectures of Digital Twin, Artificial Intelligence Integration, DT, Digital Twin, Human Twin
Excitation System of Hydro Power Generator
In modern hydroelectric power plants, synchronous generators are commonly used due to their ability to deliver stable and efficient electrical power. For proper operation of a synchronous generator, an excitation system is essential as it supplies direct current to the rotor winding. In high-capacity hydroelectric generating units, such as 40 MW generators, a large and reliable excitation system is required to maintain voltage stability and system performance under varying load conditions. The Lower Jurala Hydroelectric Power Plant consists of six generating units, each having a capacity of 40 MW. Due to the large rating of these generators, the excitation system must be robust and capable of providing sufficient field current. A secure and well-designed excitation system ensures smooth generator operation, improves system reliability, and enables the generator to withstand abnormal operating conditions and electrical disturbances. To protect the generator and excitation system from abnormal operating conditions, various protection schemes are provided such as voltage protection, stator current protection, over-excitation and under-excitation protection, and digital control protections. Each unit is equipped with an Automatic Voltage Regulator (AVR) and Field Current Regulator (FCR) for controlling terminal voltage and excitation current. In digital excitation systems, a small error margin known as droop, typically ranging from 0% to 5%, is maintained to ensure continuous relay operation and system stability. Since the excitation system handles high currents, it is prone to temperature rise during operation. Therefore, adequate heat sinks and cooling systems are provided to dissipate heat effectively, ensuring reliable performance and long service life of the excitation system. It also reduces maintenance requirements and improves overall system availability.
Automatic Voltage Regulator (AVR); Excitation System; Field Current Regulator (FCR); Hydroelectric Power Plant; Synchronous Generator; Thermal Management; Voltage Stability; 40 MW Generator.
Resume analysis is a multi-step process that includes both automated and manual resume reviews. The goal is to extract key information about job applicants from resumes in the most efficient way possible. Every month, some big firms may receive thousands of resumes. Automated resume screening can mitigate the impact of any potential bias and ensure that only the most qualified candidates advance. How? Because, when using a chatbot to screen resumes, you can program it to only evaluate job-specific criteria, such as the specific skills required for the role. AI technology ensures that all submissions are automatically and promptly screened, giving all applicants a fair chance, even if they are not qualified for the job role. The sooner they are informed, the sooner they can proceed. DOI: https://doi-ds.org/doilink/03.2026-71345271
Resume analysis, automated, AI technology, Resume Builder, Models
Mohammed Ismail, Mohammed Adnan Uddin, Jella Shivani, Mohammed Akber Sharfi, Kasireddy Goutham Reddy
An Insightful Deep Fake Face Detection System Using AIML
The rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) has led to the creation of highly realistic deepfake face manipulations, raising serious concerns regarding digital security, misinformation, and identity fraud. This paper presents an insightful deepfake face detection system that utilizes advanced deep learning techniques, particularly Convolutional Neural Networks (CNNs), to effectively identify manipulated facial content in images and videos. The proposed system focuses on detecting subtle inconsistencies in facial features, textures, and spatial patterns that are difficult for humans to perceive. It incorporates preprocessing methods, face extraction algorithms, and optimized classification models, along with techniques such as transfer learning and attention mechanisms to enhance detection accuracy and robustness. The model is trained and evaluated on standard datasets, demonstrating strong performance in terms of accuracy, precision, recall, and F1-score. The system is scalable and suitable for real-world applications such as social media verification, digital forensics, and cybersecurity, contributing to the prevention of deepfake-based threats and ensuring the authenticity of digital media.
Deepfake Detection, Artificial Intelligence, Machine Learning, CNN, Image Processing, Face Recognition, Transfer Learning, Attention Mechanism, Digital Forensics, Cybersecurity.
Chinnolla Narsaiah, Bakkashetti Akshitha, Banavath Naveen Naik, Dhravath saicharan teja
Leadership Styles And Conflict Management
The leadership style that a leader chooses to use has a substantial influence on the way in which they make decisions, communicate expectations to followers, motivate followers, and develop a working environment. In other words, the manner in which an individual leads other is an indication of the leadership style they use. It is a reflection of the views, interests, and attitudes of the leader on how an individual may effectively influence and lead other individuals. Leadership may take on many different forms, and each of these styles has the ability to have an impact on the dynamics, productivity, and culture of an organization or group in a number of different ways. Conflict may seem like a problem when it happens, but it really presents an opportunity for growth and a great way for groups or individuals to open up to one another. Nevertheless, conflict management must be used when disagreement impacts production and sparks other disagreements. When discussing conflict management, the goal of minimizing or eliminating conflict in whatever form is considered. "The goal of conflict management is to improve learning within an organization by minimizing the negative outcomes of conflict and promoting the positive outcomes of conflict," Rahim (2002) points out. Organizations and individuals may profit from conflict at different levels and types that are acceptable to them; this is the purpose of conflict management. Conflict management may not always include resolving disputes.
leadership , styles , conflict , management
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Customer Buying Behaviour On Selected Web Portals In Digital Marketing
Nowadays, individuals of all ages spend a lot of time on social media. These social media sites eat up everyone's time. The incredible and crucial features of these social media platforms are meant to be utilized for things like networking, sharing news, promoting brands, businesses, and politics. Brand reputation and its impact on the efficacy of a few chosen online portals was the primary focus of the present research. Various analyses were conducted, and the research's findings represent the attitude of customers about the selection of online portals carried out by these social media sites. The vast majority of people who took the survey saw the internet as an important part of their daily life and a reliable resource for news and other information. Because it contains current information, people trust online sources and think it's safe to use the internet to get their facts straight. Social networking sites, such as Facebook, would occupy a significant amount of people's leisure time, with many more following closely after. The results of the study show clearly that people pay attention to ads for portals on various social media platforms, and that these ads encourage them to keep driving. According to the results, these social media platforms are always front and center when it comes to their ultimate purchasing choice. People are more inclined to utilize the information and make decisions when they receive affordable and helpful portals or offers, such as promotions and discounts on items. Additionally, the study found that when buyers have a good attitude about e-WOM and online advertising promoting brands, they are more likely to make a purchase. When deciding how to behave, customers will take into account the ease of the cancellation policy, the simplicity of the dispute resolution process, the variety and quality of the items, and their level of comfort with all the amenities offered by the portals. Further factors that influence people's opinions of web portals include their level of self-assurance, the availability of technologically oriented services, their level of expertise and exposure, and the informational features of the portals.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
This project tackles the increasing threat posed by deepfake technology, which enables the creation of highly convincing but deceptive images and videos. Deepfakes can spread misinformation and compromise digital trust, making effective detection methods crucial. Our solution is a comprehensive deepfake detection system designed to analyze both image and video content with high accuracy. For video analysis, we utilize Gated Recurrent Units (GRUs), a type of recurrent neural network well-suited for modeling temporal sequences. GRUs capture subtle temporal patterns and inconsistencies across video frames that often indicate manipulation. For images, we employ Convolutional Neural Networks (CNNs), focusing on two well-known architectures: VGG16 and MobileNet. These CNNs excel at extracting detailed visual features, enabling reliable classification of images as either genuine or deepfakes. To make the detection accessible and user-friendly, we implement a web application where users can upload their images or videos for instant analysis and receive classification results in real time. This combination of temporal modeling with GRUs and spatial feature extraction through CNNs offers a robust approach to detecting sophisticated deepfake content. Our system is designed not only for accuracy but also for ease of use, aiming to empower individuals and organizations to verify digital media authenticity effectively. Ultimately, this project contributes toward mitigating the impact of deepfakes, helping to preserve the integrity and trustworthiness of digital media in an era where manipulation techniques are rapidly evolving.
Pendota Shylika, Maggidi Srija, Mothe Pravalika, Pesaru Srikanth
Addressing Integration Issues And Improving Stability In Hybrid Renewable Power Networks
The rapid penetration of renewable energy sources (RES) into traditional power systems is one of the major challenges to deal with in terms of system stability, security, and power quality management. Such integration in hybrid renewable power networks involves primarily exploring key integration issues in various operational aspects of hybrid renewable power networks and providing holistic solutions utilizing advanced strategies of stability enhancement. Main goals include voltage and frequency stability studies, energy storage-tied analysis and control studies of PV-WE hybrid systems and a plenty of examination among these two types of technology. The research is based on a descriptive-analytical approach that uses secondary data from government databases, statistics from IRENA and existing literature. It hypothesizes that the deployment of the energy storage systems and optimal control strategies enhances the grid stability of hybrid renewable networks substantially. Results prove that hybrid energy storage systems lead to a 35-45% cut of frequency deviations and improve voltage regulation within the required ranges. We use a global and publicly available dataset on renewable capacity statistics to show that total global capacity increased from 2537 GW in 2019 to 2799 GW in 2020, demonstrating the need to understand the accelerating integration challenges. The subsequent discussion synthesises findings about the optimal sizing, placement, and control of storage systems. Coordinated control frameworks that integrate battery energy storage with advanced power electronics considerably alleviates integration issues in hybrid renewable networks.
Hybrid Renewable Energy Systems, Grid Integration, Power System Stability, Energy Storage, Frequency Regulation
A Study of Smooth Structures in the Topology of 4-Dimensional Manifolds
The topology of 4-dimensional manifolds occupies a uniquely exceptional position in geometric and differential topology, primarily because dimension four is the only one that admits exotic smooth structures — a phenomenon absent in every other dimension. This study systematically investigates the smooth structural diversity of simply connected compact 4-manifolds through analysis of intersection forms, Seiberg-Witten invariants, Donaldson diagonalization, and knot surgery techniques. The primary objectives are to classify smooth structures using established topological invariants and to examine quantitative constraints on smooth structure existence imposed by Furuta's 10/8-inequality and the Fintushel-Stern formula. A deductive-analytical methodology is employed, drawing on verified theorems and tabulated data from the literature through 2021. Results demonstrate that manifolds homeomorphic but non-diffeomorphic to standard models proliferate via knot surgery constructions, particularly in the CP²#nCP² family. The Fintushel-Stern formula generates infinitely many exotic smooth structures indexed by Alexander polynomials of knots. These findings confirm the extreme complexity and incompleteness of smooth classification in dimension four, reinforcing the hypothesis that no finite algorithm can classify all simply connected smooth 4-manifolds.
4-manifolds, smooth structures, Seiberg-Witten invariants, Donaldson diagonalization, exotic R⁴
Brain Tumor Detection Using Image Processing and Machine Learning
Brain tumors represent one of the most critical neurological disorders affecting the central nervous system. Early and accurate detection of brain tumors is essential for effective treatment planning and improved patient survival rates. Medical imaging technologies such as Magnetic Resonance Imaging (MRI) provide detailed visualization of brain tissues; however, manual interpretation of MRI scans by radiologists is time-consuming and subject to human error. In recent years, image processing techniques combined with machine learning algorithms have emerged as efficient tools for automated tumor detection and classification. This research paper presents a systematic framework for detecting brain tumors using image processing and machine learning techniques. The proposed approach includes image preprocessing, segmentation, feature extraction, and classification stages. Techniques such as noise filtering, threshold-based segmentation, and machine learning classifiers are utilized to improve the detection accuracy. Experimental results demonstrate that machine learning models can significantly enhance the accuracy and reliability of brain tumor detection compared with traditional diagnostic approaches. The results highlight the effectiveness of the proposed methodology in assisting medical professionals in clinical decision-making.
Brain Tumor Detection, Image Processing, Machine Learning, MRI Analysis, Medical Image Segmentation.
P. Sairupa, M.satwika, P. Jyothika, N. Aravind
V2X Communication Using Machine Learning and IoT Technology
Vehicle-to-Everything (V2X) communication has emerged as a critical technology for enabling intelligent transportation systems, autonomous driving, and road safety improvements. The rapid growth of connected vehicles and smart infrastructure requires efficient communication mechanisms capable of managing high volumes of real-time data. Integrating Machine Learning (ML) and Internet of Things (IoT) technologies into V2X systems enhances decision-making capabilities, improves traffic management, and reduces accident risks. This research paper proposes a smart V2X communication framework that combines IoT sensors, vehicular communication modules, and machine learning algorithms to improve road safety and traffic efficiency. The proposed system collects real-time vehicular data such as speed, location, and traffic density through IoT devices and processes the data using machine learning models to predict traffic conditions and potential hazards. The system architecture enables vehicles to communicate with other vehicles, roadside infrastructure, pedestrians, and network servers in real time. Experimental evaluation demonstrates improvements in communication efficiency, reduced latency, and enhanced safety decision-making compared to traditional vehicular communication systems. The results indicate that integrating ML with IoT-based V2X networks can significantly improve intelligent transportation systems and support the development of autonomous vehicle ecosystems.
V2X Communication, Internet of Things, Machine Learning, Intelligent Transportation Systems, Smart Vehicles, Vehicular Networks
Aleti Rachana, Golla Priyanka, Badavath Siddu, Barigela Vignesh
Exploring Surface Wave Propagation Mechanisms in Layered Materials
Surface wave propagation in layered materials represents a fundamental domain of applied geophysics and structural engineering, with direct relevance to seismic site characterization and non-destructive evaluation. This study investigates the mechanisms governing Rayleigh and Love wave propagation across multi-layered elastic media, focusing on phase velocity dispersion, mode superposition, and depth-dependent shear-wave velocity estimation. The primary objective is to analyze how layer stiffness contrasts and thickness ratios influence dispersion curve morphology. A computational-analytical methodology was adopted, utilizing the Thomson-Haskell transfer matrix formulation applied to synthetic and field-validated datasets compiled from published geophysical surveys. The central hypothesis posits that increasing impedance contrast between successive layers significantly amplifies higher-mode Rayleigh wave contributions, complicating fundamental-mode inversion. Results derived from five structured data tables confirm that dispersion sensitivity is strongly frequency-dependent, with shallow layers dominating high-frequency response and deeper layers controlling low-frequency behavior. Statistical analysis of inverted Vs profiles yields mean errors below 2%, affirming inversion reliability. The study concludes that multi-mode surface wave analysis substantially improves subsurface velocity model accuracy compared to single-mode approaches, offering critical implications for earthquake engineering site assessment across Indian geologic settings.
Rayleigh waves, Love waves, dispersion curves, layered media, shear-wave velocity
An Analysis of Fuzzy Graph Coloring Techniques and Their Applications
Fuzzy graph coloring is a generalization of classical graph coloring that incorporates the uncertainty and imprecision inherent in real-world systems. This paper presents a comprehensive analysis of fuzzy graph coloring techniques including vertex coloring, edge coloring, total coloring, and picture fuzzy graph coloring along with their practical applications in scheduling, traffic signal optimization, network design, and resource allocation. The primary objective of this study is to systematically evaluate major fuzzy graph coloring approaches through comparative performance analysis using data sourced from published experimental benchmarks. A descriptive-analytical methodology is adopted, utilizing established fuzzy graph datasets and chromatic number results available in the literature. The central hypothesis is that fuzzy graph coloring techniques outperform classical chromatic methods when the underlying relational data is inherently uncertain. Results confirm that fuzzy chromatic methods reduce color usage by 15–30% compared to classical approaches in uncertain scheduling environments. The discussion aligns these findings with current applications in COVID-19 regional analysis, phishing detection, and traffic management. The paper concludes that fuzzy graph coloring constitutes a powerful, flexible tool for combinatorial optimization under uncertainty.
Fuzzy graph, chromatic number, graph coloring, α-cut, combinatorial optimization
Computational Study of Reaction Mechanisms in Organic Chemical Reactions
Computational chemistry has emerged as a transformative approach for elucidating mechanistic pathways in organic reactions where direct experimental observation of transition states remains experimentally inaccessible. This study computationally investigates five fundamental organic reactions SN2 nucleophilic substitution, E2 elimination, Diels-Alder cycloaddition, aldol condensation, and acid-catalyzed esterification employing Density Functional Theory (DFT) at the B3LYP/6-311+G(d,p) level using Gaussian 16 software. The principal objectives are to determine activation energies, characterize transition state geometries, and compare frontier molecular orbital (HOMO-LUMO) parameters as reactivity predictors across mechanistically distinct reactions. The central hypothesis is that DFT computed at B3LYP/6-311+G(d,p) predicts experimental activation barriers within ±2.5 kcal/mol. Results from six data tables reveal activation barriers between 12.6 and 24.5 kcal/mol, HOMO-LUMO gaps ranging from 3.31 to 4.23 eV, and Natural Bond Orbital (NBO) charge distributions consistent with established mechanistic frameworks. Functional benchmarking confirms that ωB97XD achieves the lowest mean absolute deviation of 1.1 kcal/mol. These findings validate DFT as a quantitatively reliable mechanistic tool with direct implications for pharmaceutical design and synthetic strategy.
Density Functional Theory, Reaction Mechanism, Transition State, Activation Energy, HOMO-LUMO Gap
NRF24L Wireless Robot for Live Human Detection in Rescue Operations
Disaster-prone environments such as collapsed buildings, fire-affected zones, industrial accident sites, and earthquake-hsit regions demand rapid and accurate human detection to increase survival rates. Traditional rescue operations expose first responders to severe risks, including structural instability, toxic gases, and limited visibility. Rescue robotics has emerged as a transformative solution to mitigate such dangers [12], [18]. This paper presents the design and development of an NRF24L01-based wireless rescue robot integrated with real-time human detection and live video streaming capabilities. The system utilizes an Arduino Mega 2560 as the central control unit, ensuring efficient motor control and wireless communication. The NRF24L01 transceiver module provides low-latency, long-range communication suitable for obstructed disaster environments [14], [17]. An ESP32-CAM module enables real-time Wi-Fi video streaming and onboard face detection, improving victim identification accuracy compared to conventional PIR-based systems [15]. The proposed robot offers a cost-effective, scalable, and reliable solution for search-and-rescue operations, minimizing human risk while enhancing operational efficiency.
Landslide monitoring, ESP32, IoT, Soil moisture sensor, Rainfall detection, Vibration sensor, Early warning system.
Chitti Tejasri, Erri Sai Kiran, Azmeera Harikrishna, Chinthareddy Tejasri
All-Terrain Robot For Military Applications
Unmanned Ground Vehicles (UGVs) play a critical role in modern military and disaster-response missions by minimizing human exposure to hostile and hazardous environments. This research presents the comprehensive design, development, and evaluation of a hardware-centric all-terrain tracked robot intended for military field operations. Unlike conventional programmable robotic platforms, the proposed system eliminates microcontroller-based motion control and instead employs a radio frequency (RF) transmitter–receiver architecture combined with servo-to-PWM conversion and high-current BTS7960 motor drivers. The robot integrates a regulated power management system, differential steering mechanism, and modular structural design to ensure operational reliability across rugged terrains. Experimental analysis demonstrates stable mobility on sand, gravel, mud, and inclined surfaces up to 35°, with efficient energy utilization and minimal signal latency. The results validate that the proposed architecture provides a cost-effective, robust, and field-repairable alternative for military reconnaissance and support missions.
Unmanned Ground Vehicle, All-Terrain Robot, Military Robotics, Differential Steering, BTS7960 Motor Driver, RF Control System, Power Management, Tracked Vehicle.
B. V. V. Surya Pravallika, Boddupally Anil Kumar, Pitta Adithya, Anumula Pruthvi
Reversible Data Hiding In Images Using Dual Embedding And Pixel Prediction
Reversible Data Hiding (RDH) has become an essential research area in digital image security because it allows confidential information to be embedded into digital images while ensuring perfect recovery of both the hidden data and the original cover image. Unlike traditional steganography and watermarking techniques that introduce permanent distortion, RDH guarantees complete reversibility, making it highly suitable for sensitive domains such as medical imaging, military communication, forensic analysis, and digital archiving. Over the years, various RDH techniques such as Difference Expansion, Histogaaram Shifting, and Prediction Error Expansion have been proposed to enhance embedding capacity and minimize distortion [14], [6], [15]. However, achieving an optimal balance between embedding capacity, visual fidelity, and computational simplicity remains a challenge. This research proposes a dual embedding framework that integrates Prediction Error Expansion (PEE) with Least Significant Bit (LSB) substitution to improve embedding efficiency while preserving high image quality. Pixel prediction using a Median Edge Detector (MED) predictor is employed to reduce prediction errors and minimize embedding distortion. The proposed method is evaluated using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index Measure (SSIM). Experimental results demonstrate that the proposed technique achieves PSNR values above 57 dB even at higher payload capacities, outperforming traditional histogram shifting and standalone LSB approaches [1], [6], [14]. The findings confirm that the proposed dual embedding approach effectively balances capacity, imperceptibility, and reversibility.
Reversible Data Hiding, Prediction Error Expansion, Least Significant Bit, Pixel Prediction, Image Security, PSNR, SSIM, Data Embedding.
Aledhandi Sheshank, Sachin Bemanavena, A. Mohammed Wajid, Chandrakanti Likith Rao
Oil Skimming and Garbage Collection Robot with Bluetooth Control, Water Quality Monitoring
Water pollution caused by oil spills and floating waste represents a persistent environmental challenge affecting aquatic ecosystems, biodiversity, and human health. Mechanical oil skimming remains one of the most reliable large-scale methods for recovering spilled oil from water surfaces, while embedded systems and Internet of Things (IoT) technologies have enabled real-time environmental monitoring and automation. This research presents the design and implementation of a low-cost Oil Skimming and Garbage Collection Robot based on the Raspberry Pi Pico microcontroller, integrating Bluetooth-based remote control and water quality monitoring. The proposed system combines a pump-based oil skimming mechanism, a front-mounted mesh garbage collector, and turbidity and temperature sensors for environmental assessment. Bluetooth Low Energy communication ensures efficient wireless control and data transmission. The system is designed as a compact, energy-efficient, and scalable solution suitable for lakes, ponds, and industrial effluent zones. The integration of mechanical cleaning and IoT-based sensing addresses the limitations of existing standalone systems. Experimental results demonstrate effective debris removal, oil collection, and accurate sensor data transmission, validating the feasibility of the proposed hybrid approach.
Oil skimmer, Raspberry Pi Pico, Bluetooth Low Energy, IoT, Water quality monitoring, Turbidity sensor, Garbage collection robot, Embedded systems.
C. Sowmya, B. Akhil, Ch. Ashwini, B. Sreeshanth
Computational Drug Design and SAR Analysis of Newly Developed Heterocyclic Compounds as Prospective Antidiabetic Molecules
Diabetes mellitus (DM) represents one of the fastest-growing metabolic disorders globally, with type 2 diabetes mellitus (T2DM) accounting for approximately 95% of all cases. The present study investigates the computational drug design approach and structure-activity relationship (SAR) analysis of newly developed heterocyclic compounds as prospective antidiabetic molecules targeting key enzymes, namely α-glucosidase, α-amylase, and dipeptidyl peptidase-IV (DPP-IV). The primary objectives were to evaluate the binding affinity, drug-likeness, and pharmacokinetic profiles of selected heterocyclic scaffolds including thiazolidinediones, oxadiazoles, benzimidazoles, and thiadiazoles through molecular docking and ADMET prediction. The methodology employed in silico approaches comprising molecular docking using AutoDock Vina, SwissADME for drug-likeness, and ProTox-II for toxicity prediction. It was hypothesized that heterocyclic compounds bearing electron-withdrawing substituents would demonstrate superior binding affinity against antidiabetic target enzymes. Results revealed that compounds with para-substituted halogen and nitro groups exhibited binding energies ranging from −7.8 to −10.6 kcal/mol, surpassing the standard drug acarbose (−6.7 kcal/mol). SAR analysis confirmed that the nature and position of substituents critically influence antidiabetic potency. In conclusion, the study validates computational drug design as an effective strategy for identifying potent heterocyclic antidiabetic leads.
Computational Drug Design, Structure-Activity Relationship, Heterocyclic Compounds, Antidiabetic, Molecular Docking
An Ethnobotanical Assessment of Plant-Based Veterinary Remedies Practiced by Indigenous Communities in Bastar
The present study was conducted to assess the ethnobotanical knowledge of plant-based veterinary remedies practiced by indigenous communities in Bastar division, Chhattisgarh, India. The primary objectives were to document the diversity of medicinal plants used in ethnoveterinary practices, identify the dominant plant families and plant parts employed, and evaluate the informant consensus on disease categories treated. A cross-sectional ethnobotanical survey was adopted employing semi-structured interviews, focus group discussions, and participatory field walks among 120 informants from Gond, Muria, Maria, and Bhattra tribal communities across 15 villages during 2019–2021. The hypothesis posited that indigenous communities of Bastar possess a rich and structured system of plant-based veterinary healthcare that remains largely undocumented. Data were analyzed using informant consensus factor (Fic), use value (UV), and fidelity level (FL). The results revealed that 58 medicinal plant species belonging to 34 families were utilized for treating 26 veterinary ailments in cattle, goats, and poultry. Fabaceae and Solanaceae emerged as the dominant families, while leaves were the most frequently used plant part (38.6%). The highest Fic was recorded for gastrointestinal disorders (0.89), followed by dermatological ailments (0.85). It is concluded that the Bastar region harbors significant ethnoveterinary knowledge that warrants systematic documentation, pharmacological validation, and integration into primary animal healthcare delivery systems.
Ethnobotany, Ethnoveterinary medicine, Indigenous communities, Bastar, Medicinal plants
The rapid expansion of e-commerce, emergency supply networks, and smart logistics infrastructure has accelerated the demand for reliable heavy-lift autonomous aerial delivery platforms. This paper presents the design, implementation, and performance evaluation of an AI-enabled hexacopter UAV optimized for medium-payload transportation. The proposed system integrates a Pixhawk-based PX4 autopilot, high-thrust BLDC propulsion, and a smart hybrid energy management framework combining Li-Po battery monitoring with predictive energy modeling. An M8N GPS module with sensor fusion-based Extended Kalman Filtering ensures precise waypoint navigation, while an onboard AI-based energy prediction model estimates real-time endurance under dynamic payload conditions. To enhance operational intelligence, the platform incorporates computer vision-based obstacle detection, LoRa telemetry for long-range communication, adaptive PID tuning, and geofencing-enabled failsafe mechanisms. Experimental evaluation demonstrates stable payload lifting up to 2.5 kg with controlled thrust margins and predictable energy consumption behavior. Flight endurance ranged from 19 minutes (no payload) to 9 minutes (2.5 kg payload), validating thrust-to-weight optimization and power management efficiency. The developed prototype demonstrates the feasibility of integrating hybrid energy awareness, AI-driven flight optimization, and autonomous mission intelligence for next-generation smart logistics applications, especially in remote and infrastructure-constrained environments.
Hexacopter UAV, AI-Based Energy Prediction, Hybrid Power Management, Autonomous Delivery, Pixhawk PX4, Smart Logistics, Heavy Payload Drone, Vision-Based Obstacle Avoidance, Multirotor Control.
Badiage Prabhakar, Bobbili Mahith, Akhilesh chennoju, Buddarthi Shiva rama Krishna