Journal of Science Engineering Technology and Management Science

The exponential growth of social media has led to the continuous generation of vast amounts of usergenerated textual content, necessitating efficient automated techniques for meaningful analysis. The release of The Social Dilemma triggered extensive global engagement, producing a large volume of tweets that capture varied public perspectives on issues such as digital ethics, platform governance, and regulatory concerns. Traditional manual analysis methods are inadequate for handling such large-scale and dynamic datasets due to limitations in time, efficiency, and scalability. To overcome these challenges, this study presents an integrated Natural Language Processing (NLP) framework designed for concurrent sentiment detection and topic categorization. The workflow begins with structured preprocessing, including tokenization, stop word elimination, and lemmatization, followed by Exploratory Data Analysis (EDA) to identify underlying textual patterns and distribution trends. Context-aware semantic features are extracted using RoBERTa-based embeddings, enabling a richer representation of textual meaning. To address data imbalance issues, the Synthetic Minority Oversampling Technique (SMOTE) is incorporated, ensuring fair representation across different classes. The processed features are then used to train multiple baseline classifiers, such as Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), facilitating comparative performance analysis. Furthermore, a Deep Neural Network (DNN) is utilized as a feature extractor, and its learned representations are enhanced through a Tree Alternating Optimization (TAO) Tree Classifier. The proposed Social Transform Deep Tree (STDT) framework effectively categorizes sentiments into Negative, Neutral, and Positive, while also identifying key thematic topics.

Keywords : Sentiment Analysis, Topic Classification, Natural Language Processing (NLP), Social Media Analytics, Text Classification, Social Transform Deep Tree (STDT).

Author : K. Anusha Reddy, K. Vamshee Krishna, Chinthakindi Koushika, Racherla Harshith, Shivuni Ganindra

Title : Robust RoBERTa-Based NLP Framework for Dual-Target Sentiment and Topic Classification in Social Media Discourse

Volume/Issue : 2026;03(04(1))

Page No : 1-11

In contemporary organizations, employee reviews shared on platforms such as Amazon represent a valuable source of insight into workplace dynamics; however, deriving meaningful information from this largely unstructured textual data remains a significant challenge. Conventional approaches, including manual inspection, heuristic-based analysis, and simple keyword-driven sentiment techniques, are not only time-intensive and inconsistent but also inadequate for capturing the nuanced context and multiple dimensions embedded within employee feedback. As a result, organizations face difficulty in accurately assessing critical workforce factors such as work-life balance, career progression, and job security when dealing with large volumes of reviews. These traditional methods lack scalability, struggle with complex sentiment interpretation, and often yield suboptimal accuracy, underscoring the necessity for a more advanced and intelligent solution. To overcome these limitations, the proposed system employs a transformer-based Natural Language Processing (NLP) framework integrated within a real-time Flask web application, utilizing Sentence-BERT (SBERT) embeddings for deep semantic feature extraction alongside a Stochastic Gradient Descent (SGD) based multi-label classification model, with a Hashing Vectorizer combined with Bernoulli Naive Bayes (BNB) serving as a comparative baseline. The framework executes a comprehensive pipeline encompassing data preprocessing, feature engineering, model training, prediction, and evaluation, enabling the simultaneous identification of multiple workplace experience indicators from a single review. By leveraging contextual understanding and scalable automation, this approach enhances prediction accuracy, minimizes manual intervention, and delivers actionable insights, thereby supporting datadriven human resource analytics and informed organizational decision-making

Keywords : Employee Reviews, Workplace Dynamics, Unstructured Text Analysis, Sentiment Analysis, Work-Life Balance, Career Progression, Job Security, Human Resource Analytics, Textual Data Processing

Author : K. Sunil Kumar,M. Ramana Kumar,Ammana Saketh reddy, Guguloth Santhosh, Dubbaka Saikiran

Title : A Transformer-Based Multi-Label NLP Architecture for Extracting Workforce Experience Indicators from Amazon Employees Data

Volume/Issue : 2026;03(04(1))

Page No : 12-23

Autonomous vehicular communication systems play a vital role in intelligent transportation by supporting continuous data exchange among vehicles, roadside infrastructure, and centralized networks. These systems depend on key parameters such as Random Access Memory (RAM), storage capacity, transmission rate, and trust factor to ensure efficient and reliable communication. In dynamic environments, accurate evaluation of communication unit capability is essential for maintaining performance, safety, and optimal resource utilization. Conventional assessment methods rely on manual configuration checks and thresholdbased monitoring, where parameters are evaluated individually. Such approaches fail to capture the complex relationships among multiple factors, making them unsuitable for real-time vehicular scenarios and leading to inaccurate capability estimation. To improve automation, machine learning models including Decision Tree Regressor (DTR), Orthogonal Matching Pursuit Regressor (OMPR), and K-Nearest Neighbors Regressor (KNNR) are used to predict capability scores. However, these models have limitations in handling nonlinear interactions, noise sensitivity, and generalization. To address these issues, a hybrid Deep Fuzzy Regression (DFR) model is proposed, integrating Deep Fuzzy Encoding (DFE) with Random Forest Regressor (RFR) and Linear Regression (LR) through ensemble learning. The system follows a pipeline of preprocessing, feature handling, training, and evaluation using MAE, MSE, RMSE, and R². Results show that DFR provides accurate and reliable capability assessment for real-time vehicular communication systems.

Keywords : Autonomous vehicular communication, intelligent transportation systems, vehicular networks, communication capability assessment, resource optimization, transmission rate, memory management.

Author : K. Anusha Reddy, Pabbathi Upender, Gunjolu Sravani, Vanam Sathya Sri Asritha

Title : Cognitive Fuzzy Learning with Continuous Regression for Online Capability Inference in Vehicular Systems

Volume/Issue : 2026;03(04(1))

Page No : 24-31

Contemporary defense systems are increasingly dependent on intelligent technologies to enhance surveillance, reconnaissance, and strategic decision-making capabilities. The rapid growth of visual data generated from satellites and unmanned aerial platforms has made manual image analysis and traditional rule-based approaches inefficient, labor-intensive, and unsuitable for large-scale processing. To overcome these limitations, the proposed system employs advanced Machine Learning and Deep Learning techniques to automate the classification of military imagery. The framework integrates multiple algorithms, including Perceptron, Decision Tree Classifier (DTC), Deep Neural Networks (DNN), and a Hybrid Convolutional Recurrent Model (CRM). The CRM combines Convolutional Neural Networks (CNN) for robust spatial feature extraction with Long Short-Term Memory (LSTM) networks to model complex sequential relationships within the extracted features. These models are trained on well-structured military image datasets to accurately categorize assets into predefined strategic classes. Experimental results demonstrate that DNN and CRM models deliver improved accuracy and reliability, depending on dataset characteristics and training conditions. Additionally, the system is equipped with a user-friendly graphical interface that allows seamless dataset uploading, model training, prediction generation, and visualization of performance metrics. By consolidating multiple models into a single platform and identifying the most effective one for deployment, the proposed solution significantly improves the efficiency, accuracy, and robustness of automated military image analysis, thereby enhancing intelligence operations and supporting informed decision-making.

Keywords : Image Analysis, Surveillance Systems, Defense Intelligence, Pattern Recognition, Automated Classification, Deep Learning.

Author : M. Ramana Kumar, Polasa Vaishnavi, Dyagala Varshith, Mohammed Sohail

Title : AI-Driven Tactical Military Asset Recognition for Enhanced Situational Awareness and Command Decision Support

Volume/Issue : 2026;03(04(1))

Page No : 32-41

The rapid evolution of digital healthcare solutions has created a critical need for systems that can manage Electronic Health Records (EHR) securely while supporting intelligent decision-making. Accurate prediction of heart disease and effective handling of patient data require both advanced analytical models and strong privacy protection, which traditional centralized systems fail to provide due to risks such as data breaches, unauthorized access, and lack of transparency. Moreover, these systems do not effectively combine predictive analytics with secure data sharing, limiting their use in real-time healthcare scenarios. To overcome these challenges, this work presents a secure healthcare framework that integrates Machine Learning (ML), blockchain technology, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The system processes a heart disease dataset using preprocessing techniques such as missing value handling, normalization with Standard Scaler, and traintest splitting, followed by prediction using Random Forest (RF) and Support Vector Machine (SVM), with performance evaluated through accuracy, precision, recall, and F1-score. A web-based interface is developed using Django to facilitate interaction between doctors and patients. For enhanced security, patient data is encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES)-based CP-ABE before being stored on the blockchain through Web3, while smart contracts manage user registration, EHR storage, and controlled access. This integrated approach ensures secure data sharing, reliable prediction of heart conditions, and transparent record management, ultimately improving data security, prediction accuracy, and enabling decentralized, tamper-proof healthcare services

Keywords : Electronic Health Records (EHR), Heart Disease Prediction, Machine Learning (ML), Blockchain Technology, Elliptic Curve Integrated Encryption Scheme (ECIES)

Author : Pulime Satyanarayana, Chandur Vedhasri, Kolugoori Gunasagar, Bhoopathi Mani Kumar

Title : Lightweight Secure and Transparent Ensemble Learning for IntegrityPreserved Health Records

Volume/Issue : 2026;03(04(1))

Page No : 42-51

Urban environments generate complex acoustic landscapes with overlapping sound events such as sirens, horns, and traffic noise, making automated monitoring for smart cities, public safety, and noise pollution control highly challenging. Traditional sound classification systems rely on hand-crafted features like Mel-Frequency Cepstral Coefficients (MFCCs) and log-Mel spectrograms, combined with models such as Support Vector Machines (SVMs), Random Forests (RF), or shallow Convolutional Neural Networks (CNNs), achieving only modest accuracy. Emerging from early 2010s research and benchmarked on datasets like UrbanSound8K, these approaches suffer key limitations: inability to capture semantic audio understanding in overlapping conditions, neglect of multi-label correlations, lack of interpretability in deep models, and dependence on command-line interfaces that limit usability for non-experts. This research proposes a Whisper-powered multi-task urban sound classification system with an integrated Graphical User Interface (GUI) built using Tkinter and secured with rolebased authentication (Lightning Memory-Mapped Database (LMDB) and SHA-256 hashing). It leverages OpenAI Whisper-base as a feature extractor, generating robust representations through mean pooling of encoder hidden states from class-organized audio data. These features are used to train four interpretable models: Boosted Rules Classifier (BRC), Hierarchical Structural (HS) Tree Classifier, Sparse Linear Integer Model (SLIM), and Marginal Shrinkage Linear Trees (MSLT), enabling dualtask classification of primary categories and subcategories. The system enhances transparency, usability, and performance by combining transformer-based representations with interpretable machine learning. It democratizes access to explainable artificial intelligence (AI) for urban monitoring, enabling secure, visual, and multi-task sound analysis for real-world smart city applications.

Keywords : Urban sound classification, acoustic scene analysis, environmental noise monitoring, overlapping sound events, smart cities, public safety, noise pollution control, semantic audio understanding.

Author : P. Satyanarayana, Thupakula Rasagna, Sahithya Ayla, Md Umer

Title : A Role-Authenticated Interpretable AI System for Multi-Tiered Urban Noise Monitoring in Smart Cities

Volume/Issue : 2026;03(04(1))

Page No : 52-62

Industry and brand classification was earlier done using manual taxonomies and rule-based systems. These methods were slow and needed a lot of human effort. They often made mistakes with similar brand names or overlapping industries. Traditional machine learning models improved speed but still needed manual feature design. They could not fully understand the meaning of words in context. With new deep learning methods, transformers brought a big change. Robustly optimised bi-directional encoder representation with transformers pretrained approach (RoBERTa) is one such model that reads text with better understanding. In this research, Robustly optimised bi-directional encoder representation with transformers pretrained approach (RoBERTa) taxanomy, uses this model for automated industry and brand classification. It reduces human work and gives more accurate results. This shows how automation with NLP is better than old manual approaches. Robustly optimised bidirectional encoder representation with transformers pretrained approach (RoBERTa) Taxonomy is an automated product classification system designed to identify the industry, brand, and category of any product text using transformer-based Natural Language Processing (NLP). This research leverages Robustly optimised Bi-directional Encoder Representation with Transformers pretrained approach (RoBERTa), a state-of-the-art transformer model, fine-tuned on labeled product data collected from titles, descriptions, and reviews. The system processes raw text through cleaning, tokenization, and multi-task classification layers that independently predict industry, brand, and hierarchical product categories. By mapping predictions into a structured taxonomy, the model provides accurate and consistent product tagging

Keywords : Industry Classification, Brand Classification, Product Categorization, Taxonomy Mapping, Natural Language Processing (NLP), Transformer Models

Author : K. Chiranjeevi, B. Poojitha, K. Balakrishna, N. Siva Nagamani

Title : RoBERTa-Based Deep Learning Approach for Industry and Brand Classification Systems

Volume/Issue : 2026;03(04(1))

Page No : 53-67

The rapid growth of the telecommunications sector has resulted in massive volumes of customer-agent interaction data, making manual sentiment analysis infeasible. This research presents a robust framework for telecom transcript sentiment detection by combining advanced Natural Language Processing (NLP) techniques, transformer-based embeddings, and ensemble Machine Learning (ML). The system begins with data preprocessing, including text cleaning, tokenization, stopword removal, and lemmatization, followed by exploratory analysis using word clouds, document length distributions, POS tagging, and bigram frequency plots to uncover textual patterns. Google Pathways Language Model (PaLM) like embeddings are then extracted using transformer models to capture rich contextual semantics, and class imbalance is addressed using Random Under Sampler for uniform representation across sentiment classes. Multiple ML models, including Logistic Regression Classifier (LRC), Decision Tree Classifier (DTC), Extra Trees Classifier (ETC), Boosted Rules Classifier (BRC), and a custom Fast Interpretable Greedy-Tree Sums (FIGS) ensemble classifier, are trained and evaluated, with the FIGS model aggregating predictions from base learners to enhance accuracy, robustness, and generalization. The framework supports real-time prediction, model persistence, and visualization of performance metrics, providing interpretable insights for telecom operations. Evaluation results, including accuracy, precision, recall, and F1-score, demonstrate the effectiveness of the proposed approach. The system offers a scalable, efficient, and interpretable solution for automated sentiment detection in telecom transcripts, enabling service providers to improve customer experience, monitor agent performance, and make data-driven operational decisions.

Keywords : Telecommunications, Sentiment Analysis, Natural Language Processing, FIGS Classifier, Real-Time Prediction

Author : R. Deepthi, N. Divya Sruthi, Shaik Sameera, Thuremerla Lahari, Siginal Neelima, Upputuru Sushma

Title : FIGS - Driven Contextual Sentiment Analysis for Large Scale Telecom Conversations

Volume/Issue : 2026;03(04(1))

Page No : 68-77

In modern railway networks, real-time monitoring and secure control communications are essential for ensuring operational safety, efficiency, and reliability. Critical operations such as signal control, train scheduling, and automated track switching generate large volumes of data that must be analyzed instantly to prevent failures or detect malicious intrusions. Traditional systems rely on manual inspections or rule-based approaches, which are slow, error-prone, and inadequate for handling the dynamic and high-volume nature of modern rail communication data. These limitations highlight the need for a robust, automated anomaly detection framework capable of accurate real-time classification. Existing methods, including Decision Tree with Cost Complexity Pruning (DTCCP) and Deep Neural Decision Tree (DNDT), provide interpretable models with moderate predictive performance. However, DTCCP often suffers from overfitting when dealing with complex sequential data, while DNDT may struggle to capture subtle contextual relationships, resulting in missed anomalies or false alarms. To address these issues, this research proposes a RuleFit (RF) classifier that combines linear rules with decision tree logic and semantic embeddings derived from Sentence-BERT (SBERT). This hybrid approach enables the system to learn both hierarchical decision boundaries and contextual patterns effectively. Performance evaluation using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC curves demonstrates that the proposed method significantly improves anomaly detection while reducing false positives and supporting reliable, real-time rail communication monitoring

Keywords : Railway Communication Security, Real-Time Monitoring, RuleFit Classifier (RF), Intelligent Transport Systems (ITS), Cybersecurity in Rail Networks

Author : Sk. Asiff, R. Deepthi, Shaik Ishaq, Shaik Abdul Bhasha, Konisetty Manoj Kumar, Yakasiri Penchala Prasad

Title : Context-Aware Rule Ensemble Learning for Adaptive Threat Detection in Intelligent Railway Signaling Data Streams

Volume/Issue : 2026;03(04(1))

Page No : 78-87

Crop disease detection is a critical challenge in modern agriculture that directly impacts food security and crop yield worldwide. This Project presents an embedded system for real-time crop disease detection using a Raspberry Pi microcomputer integrated with a USB camera and a deep Convolutional Neural Network (CNN) model trained on a large dataset of diseased and healthy plant images. The proposed system captures images of plant leaves in the field, preprocesses them, and feeds them through the CNN model to classify the type of disease with high accuracy. Results obtained from the system are displayed on a connected LCD screen and simultaneously transmitted to a remote user via a Telegram Bot for immediate notification. making it a cost-effective and practical solution for real-time agricultural disease monitoring and early intervention.

Keywords : Crop Disease Detection, Convolutional Neural Networks, Raspberry Pi, Deep Learning, Image Classification, Telegram Bot, LCD Display, Embedded Systems, Plant Pathology, Precision Agriculture

Author : G. Udaykiran Bhargava, B Tejaswi, L Hemanth, B Dharma Teja, B Sai Chandhu, Sk Aman

Title : CROP DISEASE DETECTION USING RASPBERRY PI CAMERA AND DEEP CNN’S

Volume/Issue : 2026;03(04(1))

Page No : 88-95

Rescuing individuals trapped in deep, narrow borewells is an extremely complex task due to limited space, absence of light, and the impracticality of direct human involvement. This research presents a compact and wirelessly controlled Borewell Rescue Robot (BRR) designed to improve safety and efficiency during such emergencies. The system is built around an ESP32-CAM module, which combines processing capability with onboard imaging to deliver real-time video feedback from inside the borewell. The robot is capable of transmitting live video using a lightweight communication protocol, enabling operators to monitor conditions and guide actions with minimal delay. A pair of hightorque servo motors forms the core of the mechanical system, allowing precise movement and controlled gripping of the victim within confined vertical shafts. To address visibility challenges, an adjustable Light Emitting Diode (LED) lighting unit is integrated, providing sufficient illumination even in completely dark environments. The control system is developed using a non-blocking, eventdriven architecture that allows simultaneous handling of motion commands and video streaming. This ensures smooth operation without lag or interruption. The robot functions through a self-contained wireless network, making it suitable for deployment in remote or infrastructure-limited areas. To enhance reliability, a safety mechanism is included that automatically stabilizes the system and halts movement if communication is disrupted. This solution offers a practical, scalable, and affordable approach to improving rescue operations in hazardous underground situations.

Keywords : Borewell Rescue Robot (BRR), ESP32-CAM MCU, Real-Time Video Streaming, Emergency Robotics, WebSocket Communication

Author : Md. Sharmila, P. Tejaswini, B. Nandini, P. Aravindh, M. Shiva Parvathi, B. Vijay Kumar

Title : A Low-Latency Visual Feedback Driven Robotic Mechanism for Rescue Operations in Restricted Vertical Structures

Volume/Issue : 2026;03(04(1))

Page No : 96-104

Predictive maintenance is a critical component of smart manufacturing in the industry 4.0, as it employs machine learning algorithms and data from IoT sensor networks to anticipate equipment malfunctions in advance. The paper examines how artificial intelligence (AI)-controlled predictive maintenance (PdM) can be applied to a smart manufacturing workplace that uses Internet of Things (IoT) technology. It covers data gathering, sensor technologies, and communication protocols as well as distributed storage schemes employed to derive and handle big industrial data. An automotive parts manufacturing plant case study used sensor data in real-time, such as temperature, vibration, acoustics and electrical data, and also used maintenance records to build predictive models. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models were designed and tested after a thorough data preprocessing and feature engineering. As shown by experimental results, LSTM was the most effective of CNN and other baseline models, with the highest accuracy (ACC) (88.1%), precision (PRE) (92.5%), recall (REC) (89.3%), and F1-score (F1) (90.9%). The implementation of the offered AI-based PdM system has led to the 24% of reduction of unscheduled downtime, 18% growth in equipment use, and 30% decrease in emergency maintenance prices. The results indicate that the incorporation of the Internet of Things-enabled sensing and sophisticated machine learning algorithms considerably boosts the reliability, operational performance, and decision-making concerning intelligent production systems.

Keywords : Predictive Maintenance (PdM), Smart Manufacturing, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning

Author : Sandeep Gupta

Title : Predictive Maintenance Framework for Smart Manufacturing using Machine Learning Models: A Case Study

Volume/Issue : 2026;03(04(1))

Page No : 105-114

The fast development of digital technologies, the need to create sustainable and efficient industrial systems, has boosted the pace of the implementation of intelligent and data-driven paradigms. When it comes to this, Industry 4.0 has made it easier for many industries to use cutting-edge tech, which has increased efficiency, flexibility, and the ability to make decisions in real-time. Among these, digital twins (DT) and edge artificial intelligence (Edge AI) stand out as game-changers for building intelligent and flexible systems. The current paper provides an overview of Edge AI architecture with its layers design, real-time processing, and advantages that include decreased latency and enhanced data privacy. The article delves into the core concepts of digital twins and how they work to create accurate digital representations of real-world systems. These concepts include the internet of things (IoT), cloud computing, artificial intelligence (AI), and extended reality (ER). The paper goes on to talk about how Digital Twin systems may use Edge AI to offer predictive maintenance, efficient resource management, and ongoing synchronization. Moreover, the emerging trends and challenging issues, including the question of scalability, interoperability, security and resource constraint, are evaluated. Lastly, the research offers information on the future research path in terms of developing powerful and smart systems.

Keywords : Edge AI, Digital Twin, Artificial Intelligence, Cloud Computing, Latency, IoT.

Author : Bhalchandra Bapat

Title : A REVIEW OF ARCHITECTURAL FRAMEWORKS AND TRENDS FOR EDGE ARTIFICIAL INTELLIGENCE AND DIGITAL TWIN TECHNOLOGIES

Volume/Issue : 2026;03(04(1))

Page No : 115-123

The In-Flight Entertainment (IFE) systems have now become a multi-dimensional and data-driven system, which not only improves passenger experience, but also supports the airline operations, creating and analyzing data in real-time. This survey provides a comprehensive description of data acquisition and logging systems incorporated in current IFE systems, their architecture, techniques and concerns. It explores the primary data sources, such as passenger contacts, system sensors, and network traffic, and focuses on how embedded software agents, monitoring tools, and middleware contribute to efficient data collection. The paper also examines communication protocols used, such as TCP/IP and HTTP/HTTPS, and lightweight messaging systems that help ensure the correct transmission of data in both onboard and air-to-ground environments with limited resources. The paper also examines centralized and distributed logging architectures, as well as real-time and batch logging designs, to effectively manage data. Such critical issues as limited bandwidth, data heterogeneity, hardware limitations, and security issues are also addressed. Recent literature is also surveyed to identify gaps in research, particularly in integrated and scalable frameworks. Overall, this work provides insights into improving data-handling strategies for next-generation intelligent IFE systems.

Keywords : In-Flight Entertainment (IFE), Data Acquisition, Data Logging, Aircraft Connectivity, Passenger Experience, Network Protocols.

Author : Mr. Sachin Manekar

Title : A SURVEY ON DATA ACQUISITION AND LOGGING MECHANISMS IN IN-FLIGHT ENTERTAINMENT (IFE) SYSTEMS

Volume/Issue : 2026;03(04(1))

Page No : 124-130

The rapid advancement of Artificial Intelligence (AI) and automation is transforming the way organizations operate, compete, and are managed in the digital era. These technologies are transforming the traditional leadership models by making it possible to make decisions that are based on data, enhance the efficiency of operations, and make innovation a reality across industries. The review paper discusses how digital leadership will increase in role in organizational management in the era of AI and automation. The review paper examined the theoretical underpinnings of digital leadership, emphasizing the role of the integration of technologies and digital skills and vision in contemporary leadership. It also examines the effects of AI and automation on leadership roles, such as improved decision-making, workflow optimization, and worker redesign. Besides that, the paper outlines the main issues related to the adoption of AI, including ethical concerns, the impact of algorithms, a threat to data privacy, and the necessity of retraining employees. The literature review is done thoroughly to investigate the current studies focusing on AI-based leadership and the new models of leadership. The findings suggest that an effective digital leadership is a moderate approach that does not only entail technological skills and capabilities, but ethical leadership and management that takes into consideration human being. The paper ends with managerial implications and recommendations to organizations that seek to successfully pass through the digital transformation in the AI-enabled environment.

Keywords : Digital Leadership, Artificial Intelligence, Automation, Digital Transformation, Data-Driven Decision Making, Organizational Innovation.

Author : Dr. Srividya Aravind

Title : Digital Leadership: Managing Organisations in the Age of AI and Automation

Volume/Issue : 2026;03(04(1))

Page No : 131-138

This study involves the structural analysis and design of curved structures, such as roofs and bridges, utilizing STAAD Pro software to evaluate their performance under various load conditions, including wind and gravity loads. This design mainly stimulates from the design on CAD designs. The process entails creating a 3D model, defining geometric properties and structural loads, and conducting ananalysis to determine internal forces like bending moments and shear forces. The resultsthen inform the design of membersto meet strength and serviceability requirements, often comparing curved structures to their straight counterparts and highlighting STAAD Pro efficiency for complex geometries and large-span projects. These projects are mainly used for different types of structures in the construction field. It also proves that these types of designs give different type of view for the buildings and in their architecture point of view.

Keywords : Curved structures, STAAD.Pro, Aesthetic and Structural Efficiency, Modelling Curved Elements, Load Considerations, Comparison with Straight Structures, Performance Evaluation.

Author : Mrs. T. Shruthi1 , Mr. M. Abhinav2 , Ms. M. Sowmya3 , Mr. Y. Sunay4 ,S.Manasa5

Title : DESIGN AND PERFORMANCE EVALUATION OF CURVED STRUCTURES USING STAAD.PRO

Volume/Issue : 2026;03(04(1))

Page No : 139-144

Lightweight concrete using Expanded Polystyrene (EPS) beads is an innovative construction material designed to reduce the self-weight of structures while maintaining adequate strength and durability. In this type of concrete, conventional aggregates are partially or fully replaced with EPS beads, which are lightweight, spherical particles made from polystyrene. The inclusion of EPS beads significantly decreases the density of concrete, making it suitable for non-load bearing walls, insulation layers, partition panels, and precast elements. The main objective of using EPS beads in concrete is to improve thermal insulation, reduce dead load, and enhance workability. Due to its low density, EPS concrete also provides good sound insulation and energy efficiency in buildings. The production process involves mixing cement, fine aggregates, water, and EPS beads in proper proportions to achieve the desired strength and workability.

Keywords :

Author : Mr. J. Bala Krishna, Mr. D. Keerthana, Mrs. M. Shirisha, Mr.P.Shyam Shashank, Mrs.S.Manasa

Title : DESIGN AND CHARACTERIZATION OF LIGHTWEIGHT CONCRETE USING EXPANDED POLYSTYRENE(EPS) BEADS

Volume/Issue : 2026;03(04(1))

Page No : 145-148

Waste tyres have a substantial detrimental influence on the environment owing to their considerable volume, non-biodegradability, susceptibility to combustion, and function as a breeding habitat for mosquitoes and several other pests. The use of waste tyre rubber for the production of environmentallyfriendly rubberized bricks serves as a method for mitigating the issue of waste disposal, so contributing to the reduction of resource depletion and environmental deterioration. The aim of this study was to evaluate the practicality of using waste tyre rubber as a partial replacement for sand in the manufacturing of rubberized fly ash bricks. The purpose was to provide evidence that WTR waste may be effectively used in the construction sector. Waste tyre rubber was used as a substitute for sand, with proportions ranging from 10% to 30%. The effectiveness of the green rubberized fly ash bricks was evaluated by analyzing its compressive strength, water absorption, porosity, thermal conductivity, and microscopic properties. The results of this research suggest that the compressive strength of rubberized bricks decreased somewhat in proportion to the extent of replacement. At a sand replacement rate of 10% with waste tyre rubber the bricks exhibited the greatest compressive strength of 10.73 MPa and water absorption rate of 16.68%. The findings indicate that WTR exhibits promise as a viable option for replacing up to 30% of sand in the production of environmentally sustainable construction bricks.

Keywords : aste tyres, Rubberized bricks, Sustainable construction, Solid waste management, Eco-friendly materials

Author : Mr.Dr. D. Ajay Kumar, Mr.K. Raghavendra, Mr.V.Ganesh, Mr.B.Bhavani, Mr.S.Manasa

Title : ASSESSMENT OF STRUCTURAL PERFORMANCE OF RUBBERIZED BRICKS USING WASTE TYRE RUBBER

Volume/Issue : 2026;03(04(1))

Page No : 149-154

The rapid growth of electric vehicles in urban environments demands the development of efficient and sustainable infrastructure to support large-scale adoption. This study presents the design and analysis of a multi-level electric vehicle parking facility integrated with charging stations, specifically developed for smart city applications. The project focuses on optimizing parking layout, structural design, traffic circulation, and charging integration within a limited urban space. A systematic methodology involving site analysis, load calculations, and structural design based on Indian Standards is adopted. The facility incorporates alternating current and fast charging systems, ensuring accessibility, safety, and energy efficiency. Smart features such as digital monitoring, automated billing, and real-time occupancy tracking are also considered. The proposed model enhances land utilization, reduces congestion, and improves user convenience. The results demonstrate that integrated EV infrastructure can significantly contribute to sustainable urban mobility and support the transition toward clean transportation systems.

Keywords : electric vehicles, ev charging infrastructure, parking design, smart cities, sustainable mobility

Author : Mrs.V. Munni, Mr.S.Sandeep, Mr.V. Gowtham, Mr.M.Rajashekar,Mrs.S.Manasa

Title : Design and Analysis of EV Parking Infrastructure with Integrated Charging Stations for Smart Cities

Volume/Issue : 2026;03(04(1))

Page No : 155-162