Advanced Research & Development Journal
type: channel
description: About the Advanced Research & Development Journal The Advanced Research & Development Journal is a peer-reviewed, national-level scholarly journal committed to publishing high-quality research in science, engineering, technology, and allied disciplines. The journal serves as a platform for Indian researchers, academicians, industry professionals, and students to disseminate innovative ideas, experimental results, case studies, and technological advancements that contribute to India’s academic and industrial growth. ARD Journal emphasizes original research conducted within India and encourages submissions that address real-world challenges, emerging technologies, and practical applications relevant to the national research ecosystem. A structured editorial and peer-review process ensures academic quality, transparency, and integrity. Aim To promote and disseminate national-level research in science, engineering, and technology. To encourage innovation, applied research, and problem-solving approaches. To provide a credible and constructive platform for Indian researchers. To strengthen research culture through multidisciplinary and interdisciplinary studies. To foster collaboration among scholars, educators, and research institutions across India. Scope The scope of the Advanced Research & Development Journal includes, but is not limited to: Engineering & Technology Computer Science & Information Technology Electronics, Communication & Electrical Engineering Mechanical, Civil & Industrial Engineering Artificial Intelligence, Machine Learning & Data Science Internet of Things (IoT), Embedded Systems & Robotics Renewable Energy & Smart Technologies Science & Applied Sciences Physics, Chemistry & Mathematics Environmental Science & Sustainable Development Biotechnology, Life Sciences & Agriculture Management & Humanities (where applicable) Business Management, Commerce & Economics Education, Social Sciences & Interdisciplinary Studies Article Types Research Articles Rev…
publisher: Novara Publishers
language: en
publication name: Advanced Research & Development Journal
issn: 3139-1281
copyright: All articles published in the Journal of Engineering Excellence (JEE) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while granting permission for anyone to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or author — provided that the original work is properly cited. This open-access license ensures maximum dissemination and impact of the published research by allowing free and immediate access to scholarly work. For more details, please refer to the official license page: ???? https://creativecommons.org/licenses/by/4.0/
items: Design and Optimization of a Quaternary Signed-Digit Adder for VLSI Applications
items: DIFFERENTIAL PRIVACY-BASED PROTECTION OF USER SHOPPING PREFERENCES IN E-COMMERCE SYSTEMS
items: DEVELOPMENT OF A SMART BIDIRECTIONAL CONVERSION INTERFACE FOR RENEWABLE STORAGE SYSTEMS
items: OPTIMIZATION OF VOLTAGE STABILITY IN LOW-VOLTAGE DISTRIBUTION SYSTEMS USING INTELLIGENT TECHNIQUES
items: PERFORMANCE ANALYSIS OF A SINGLE-STAGE SOLAR INVERTER WITH INTEGRATED BOOSTING TECHNIQUE
items: A NOVEL TRANSFORMERLESS PROTECTION SCHEME FOR VOLTAGE REGULATION IN DISTRIBUTION GRIDS
items: DESIGN AND DEVELOPMENT OF AN INTEGRATED DRIVE AND CHARGING SYSTEM FOR ELECTRIC VEHICLES
items: A STRATEGIC FRAMEWORK FOR SOCIAL ENGINEERING AND HUMAN-CENTRIC CYBER THREATS
items: FEATURE LEARNING AND PRICE ESTIMATION USING DEEP LEARNING IN REAL ESTATE
items: SCALABLE AND SECURE E-LEARNING PLATFORMS USING CLOUD COMPUTING TECHNOLOGIES
items: HEART RATE VARIABILITY-BASED STRESS DETECTION USING DEEP NEURAL NETWORK MODELS
items: ADVANCED CREDIT CARD FRAUD DETECTION USING ENSEMBLE LEARNING ALGORITHMS
items: PERSONALIZED DIGITAL COUPON ISSUANCE FOR ENHANCING RETAIL REVENUE IN SHOPPING MALLS
items: EVALUATING ENERGY-EFFICIENT DRIVING PATTERNS OF ELECTRIC BUSES AT STOPS
items: FEATURE EXTRACTION AND CLASSIFICATION FOR SPAM DETECTION USING SUPERVISED ML
items: DEEP LEARNING MODELS FOR ANALYZING PUPILLARY DYNAMICS IN GENETIC DISORDER DETECTION
items: ENHANCING PUBLIC SAFETY IN SMART CITIES USING DEEP LEARNING-BASED VIOLENCE DETECTION
items: MULTI CLASS TRAFFIC OBJECT DETECTION AND SCENE UNDERSTANDING USING SUPERVISED MODELS
items: NETWORK SECURITY ENHANCEMENT THROUGH MACHINE LEARNING-BASED CYBER ATTACK DETECTION
items: BLOOM FILTER-BASED CRYPTOGRAPHIC TECHNIQUES FOR CLOUD DATA SECURITY
items: IMPROVING TRUST IN SOCIAL MEDIA PLATFORMS THROUGH ADVANCED MALICIOUS PROFILE DETECTION TECHNIQUES
items: MULTIMODAL ANALYSIS OF INTER-BRAIN AND BEHAVIORAL SYNCHRONY IN REMOTE LEARNING
items: ATTENTION ENHANCED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC ANOMALY DETECTION
items: DEEP NEURAL NETWORKS FOR SUSPICIOUS ACTIVITY RECOGNITION IN VIDEO SURVEILLANCE
items: AUTOMATED ATTENDANCE MANAGEMENT SYSTEM USING FACIAL RECOGNITION TECHNIQUES
items: A HYBRID ML FRAMEWORK FOR PERSONALITY-BASED CUSTOMER SERVICE OPTIMIZATION
items: INTEGRATING BIOINFORMATICS AND DEEP LEARNING FOR DRUG SIDE EFFECT PREDICTION
items: PREDICTIVE MODELING OF CUSTOMER PURCHASE BEHAVIOR USING RETAIL DATA MINING METHODS
items: PROFITABILITY AND RETURN ANALYSIS THROUGH GROWTH STRATEGIES AT UNION BANK OF INDIA
items: COMPENSATION STRUCTURES AT HCL TECHNOLOGIES

Design and Optimization of a Quaternary Signed-Digit Adder for VLSI Applications
type: item
description: The time required for addition is constrained by the carry propagation from the least significant bit to the most significant bit in the context of conventional binary adders. This lower bound increases in proportion to the operand's width. The quaternary signed-digit (QSD) number system, a redundant representation of radix-4 using {-3,...,3}, is used to overcome this constraint. Because each digit can be represented in several ways, addition can be done in a consistent amount of steps regardless of the number of digits. A carry-free QSD adder for VLSI applications is designed and optimized in this study. A compact three-bit two's complement digit encoding and two-step implementation are used in the adder. From each pair of digits, an intermediate carry and sum are generated. This prevents subsequent digit additions from generating a carry. All digit slices are reduced to tiny two-level networks, and the adder is a parallel array of identical slices. Due to generating logic enhancement, it has been decreased to this level. The design is compared to ripple-carry, carry-lookahead, carry-select, and redundant-binary adders in power, transistor count, and latency using a transistor-level approximation in a 45 nm CMOS flow. The normal data shown here show that worst-case addition latency is consistent from 8 to 64 digits. In contrast to binary adders' growing delay, the transistor's cost is linearly related to a ripple-carry adder. The numerical values provided are for illustration only; the reader should duplicate them using SPICE data and synthesis.
subject: Articles
  topic: section
subject: Quaternary signed digit
  topic: keywords
subject: carry-free addition
  topic: keywords
subject: redundant number system
  topic: keywords
subject: high-speed adder
  topic: keywords
subject: VLSI arithmetic
  topic: keywords
subject: low-power design
  topic: keywords
creator: RAYANCHU PAVANI
creator: D. RAVIKIRAN BABU
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-08
publication date: 2026-07-08

DIFFERENTIAL PRIVACY-BASED PROTECTION OF USER SHOPPING PREFERENCES IN E-COMMERCE SYSTEMS
type: item
description: This study investigates the potential of differentiated privacy to safeguard the purchase preferences of online consumers while simultaneously enabling data analysis and personalized services. Protecting user privacy is a challenging endeavor due to the extensive collection of customer data by online purchasing websites. This study examines the potential of differential privacy techniques to conceal customers' private purchasing habits by introducing controlled noise to data without affecting the accuracy of the analysis. It also maintains a balance between data privacy and service quality to guarantee that enterprises can acquire critical knowledge while safeguarding customer data. The results indicate that differential privacy is a dependable and efficient method for fostering user confidence, alleviating privacy concerns, and facilitating secure data exchange in contemporary e-commerce.
subject: Articles
  topic: section
subject: Differential Privacy
  topic: keywords
subject: E-Commerce Systems
  topic: keywords
subject: User Shopping Preferences
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Privacy Protection
  topic: keywords
subject: Personalized Recommendations
  topic: keywords
subject: Data Security
  topic: keywords
subject: Customer Confidentiality
  topic: keywords
creator: Mohd Khaja Ismail Uddin
creator: Mr.R. Sagar
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-07-06
publication date: 2026-07-06

DEVELOPMENT OF A SMART BIDIRECTIONAL CONVERSION INTERFACE FOR RENEWABLE STORAGE SYSTEMS
type: item
description: This initiative aims to enhance the reliability, efficiency, and energy management capabilities of existing power applications by developing a clever two-way conversion interface for renewable storage systems. The proposed system incorporates sophisticated power electronic converters and intelligent control techniques. This facilitates the easy transfer of energy between the grid, energy storage devices, and renewable sources. The interface ensures that the energy saved is utilized as efficiently as possible while maintaining a steady voltage and power quality with both charging and discharging modes. When adaptive control techniques and real-time tracking are employed, the system reacts more effectively to variations in load and generation. Additionally, the design reduces conversion losses, harmonic distortion, and overall system efficiency. Microgrids, solar photovoltaic systems, and electric car energy management all benefit from the new smart interface. Finding adaptable and durable energy solutions is made simpler as a result.
subject: Articles
  topic: section
subject: Bidirectional Converter
  topic: keywords
subject: Renewable Energy Systems
  topic: keywords
subject: Energy Storage
  topic: keywords
subject: Smart Interface
  topic: keywords
subject: Power Electronics
  topic: keywords
subject: DC–DC Conversion
  topic: keywords
creator: Naredla Abhiram
creator: Mr.S.Praveen
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-11
publication date: 2026-06-11

OPTIMIZATION OF VOLTAGE STABILITY IN LOW-VOLTAGE DISTRIBUTION SYSTEMS USING INTELLIGENT TECHNIQUES
type: item
description: This paper explores the utilization of innovative methodologies to optimize voltage stability in low-voltage distribution systems, thereby improving operational efficiency, reliability, and power quality. Modern distribution networks are confronted with the formidable challenge of stabilizing voltage profiles in response to the growing demand from electric vehicles, variable loads, and distributed energy resources. The research investigates intricate computational methods, including metaheuristic optimization algorithms, artificial neural networks, and fuzzy logic systems, to predict, monitor, and regulate voltage fluctuations in real time. In dynamic contexts, these astute techniques enable the mitigation of voltage deviations, efficient reactive power management, and adaptive decision-making. The proposed method is a strong contender for use in next-generation smart grid applications, as it outperforms conventional control strategies in terms of system stability, power loss, and resilience.
subject: Articles
  topic: section
subject: Voltage Stability
  topic: keywords
subject: Low-Voltage Distribution Systems
  topic: keywords
subject: Intelligent Techniques
  topic: keywords
subject: Artificial Neural Networks
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
subject: Metaheuristic Optimization
  topic: keywords
creator: Kola Sindhuja
creator: Mr.K.Ramesh
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-11
publication date: 2026-06-11

PERFORMANCE ANALYSIS OF A SINGLE-STAGE SOLAR INVERTER WITH INTEGRATED BOOSTING TECHNIQUE
type: item
description: The intention of this study is to examine a single-stage solar inverter that integrates boosting to improve the efficiency of energy conversion and the compactness of the system. The concept that has been proposed eliminates the necessity for a DC–DC boost converter by enhancing the voltage at the inverter stage. In addition, the overall system cost, switching inefficiencies, and the number of components are all reduced. The conversion efficiency, power quality, total harmonic distortion (THD), and voltage gain are all subjected to rigorous testing under a diverse range of solar irradiation and load conditions. The integrated control system not only improves dynamic responsiveness and MPPT efficiency, but also improves stability. Simulators and experiments have demonstrated that the inverter that has been introduced is superior to two-stage systems in terms of energy losses, voltage management, and efficiency, rendering it appropriate for use in modern solar applications.
subject: Articles
  topic: section
subject: Single-stage solar inverter
  topic: keywords
subject: Integrated boosting technique
  topic: keywords
subject: Photovoltaic systems
  topic: keywords
subject: Maximum Power Point Tracking (MPPT)
  topic: keywords
subject: Conversion efficiency
  topic: keywords
creator: Myana Anusha
creator: Mr.J.Raju
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-11
publication date: 2026-06-11

A NOVEL TRANSFORMERLESS PROTECTION SCHEME FOR VOLTAGE REGULATION IN DISTRIBUTION GRIDS
type: item
description: This work introduces a groundbreaking transformerless protection approach to improve voltage regulation in current distribution circuits. In order to maintain voltage stability regardless of changes in loads or defects, the proposed method uses sophisticated power electronic converters and astute control procedures, doing away with the necessity for conventional transformers. Adaptive compensatory mechanisms, real-time monitoring, and quick problem detection enable the system to reduce system size, cost, and energy losses, resulting in dependable operation. The proposed method successfully mitigates voltage fluctuations, increases power quality, and provides robust protection against overvoltage and undervoltage scenarios, according to both experimental and theoretical findings. The transformerless design is a great fit for next-gen smart distribution networks since it can increase system efficiency and facilitate the integration of renewable energy sources.
subject: Articles
  topic: section
subject: Transformerless protection
  topic: keywords
subject: Voltage regulation
  topic: keywords
subject: Distribution grids
  topic: keywords
subject: Power quality
  topic: keywords
subject: Fault detection
  topic: keywords
subject: Power electronic converters
  topic: keywords
creator: Elkaturi Raghu
creator: Mr.N.Kiran Kumar
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-11
publication date: 2026-06-11

DESIGN AND DEVELOPMENT OF AN INTEGRATED DRIVE AND CHARGING SYSTEM FOR ELECTRIC VEHICLES
type: item
description: The objective of this project is to construct and refine an integrated drive and charging system for electric vehicles, thereby integrating propulsion and charging operations into a single power electronics architecture. The proposed solution reduces costs, saves space, and streamlines the system by utilizing shared components such as the inverter and motor windings to manage both on-board charging and traction. The system is capable of transitioning between driving and charging modes with minimal degradation in power quality, reliability, or efficiency through the use of advanced control algorithms. The integration also enables bidirectional power transfer in vehicle-to-grid (V2G) applications, which improves energy management and reduces the necessity for supplementary hardware. The system that has been developed is a viable alternative for next-generation electric vehicles, as it outperforms conventional separate propulsion and charging systems while consuming less energy, as demonstrated by experimental and simulation results. Additionally, it is more compact and diminutive.
subject: Articles
  topic: section
subject: Electric Vehicles (EVs)
  topic: keywords
subject: Integrated Drive and Charging System
  topic: keywords
subject: Power Electronics
  topic: keywords
subject: On-board Charger
  topic: keywords
subject: Traction Inverter
  topic: keywords
subject: Bidirectional Power Flow
  topic: keywords
creator: Eru Mahesh
creator: Dr.K.Chandramouli
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-11
publication date: 2026-06-11

A STRATEGIC FRAMEWORK FOR SOCIAL ENGINEERING AND HUMAN-CENTRIC CYBER THREATS
type: item
description: Social engineering attacks are a common cybersecurity problem. Despite their prevalence and importance, few concepts and techniques fully explain social engineering assaults. The six steps of social engineering in this paper use different methods. This provides a complete foundation for examining various methods. Understanding the internal mechanisms of these attacks may help us design better defenses. According to the research, the framework is straightforward, comprehensible, and practical, similar to the MITRE ATT&CK, although it has more benefits. To demonstrate the actual applicability of this paper's strategy, the comprehensive framework is used to rigorously paper and examine a number of real-world social engineering instances.
subject: Articles
  topic: section
subject: Social engineering
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: human factor in security
  topic: keywords
subject: cybercrime
  topic: keywords
subject: cyber awareness
  topic: keywords
creator: Yara Srivani
creator: Mrs. Y. Susheela
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

FEATURE LEARNING AND PRICE ESTIMATION USING DEEP LEARNING IN REAL ESTATE
type: item
description: Taiwanese houses, flats, and other types of urban homes should be looked at very carefully. Real estate researchers and forecasters frequently implement surveys and statistical instruments to accumulate data. Nevertheless, when confronted with multidimensional data, these methodologies become inefficient and time-consuming. The objective of this initiative was to develop a real estate forecasting model that could adapt to the current environment and circumstances. The data was standardized to ensure that it was categorized correctly after being extracted from public government records. In order to guarantee that the appropriate clustering method was implemented, cross-statistical analysis was implemented. In order to enhance the precision of classification, an automatic encoder for deep learning was implemented. Double-bottom map ppaper swarm optimization (DBM-PSO) was implemented to identify the optimal clustering solution. We sought to determine the factors that contributed to the increase in property prices in Taiwan over the past decade by employing cluster analysis and deep learning on publicly available data. The findings indicate that the average unit price, the frequency of real estate transactions, and the building material and construction index all significantly influence the prices of property in Taiwan. These discoveries can assist policymakers and researchers in addressing specific aspects of real estate development and improving market regulation to prevent excessive growth. The framework that was proposed is a novel approach to analyzing and forecasting real estate trends.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: real estate
  topic: keywords
subject: particle swarm optimization algorithm
  topic: keywords
subject: economy
  topic: keywords
creator: Kuna Neelima
creator: Dr. T. Ravikumar
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

SCALABLE AND SECURE E-LEARNING PLATFORMS USING CLOUD COMPUTING TECHNOLOGIES
type: item
description: Combining cloud computing and e-learning could have positive effects, which are looked into in this Paper. This Paper aims to uncover the intricate interaction between cloud computing and online learning. The Paper investigates the intricacies of this integration through the analysis of 154 scholarly Papers, focusing on the trends and topics that have piqued interest. Architecture (27% of all courses), software (19%), and performance (18%) are just a few of the many areas of online education that cloud computing has an impact on. In virtual settings, network and storage challenges are prioritised over security concerns. The three most common categories of cloud computing services are platforms (10%), software (18%), and hardware (17%). In total, 74% are public clouds, 3% are mixed clouds, and 11% are alternate models. The Paper investigates the potential association between cloud computing and online learning. It highlights the restrictions imposed by specialised infrastructure, the challenges with private and hybrid clouds, and the scarcity of other platform and infrastructure alternatives.
subject: Articles
  topic: section
subject: Cloud computing
  topic: keywords
subject: e-learning
  topic: keywords
subject: environment
  topic: keywords
subject: educational
  topic: keywords
subject: e learning based cloud computing
  topic: keywords
subject: systematic
  topic: keywords
creator: Govindula Maruthi Kumar
creator: Mr. T. Raghupathi
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

HEART RATE VARIABILITY-BASED STRESS DETECTION USING DEEP NEURAL NETWORK MODELS
type: item
description: This research makes use of Heart Rate Variability (HRV) to gauge people's levels of stress by analyzing physiological data through models trained on deep neural networks. Readings of heart rate variability (HRV) obtained from an electrocardiogram (ECG) are employed in the investigation due to their significance as indicators of autonomic nervous system activity that are associated with stress responses. In order to enhance the accuracy and utility of stress recognition, advanced deep neural network techniques, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, are implemented in lieu of conventional machine learning methodologies. By means of feature extraction, classification, and data preparation, the proposed system is capable of distinguishing between stressful and non-stressful scenarios in real time. For instance, researchers have demonstrated that deep learning models are more adept at learning new features, making predictions, and managing intricate physiological trends. This investigation enhances healthcare monitoring systems that can identify early indicators of stress and mental illness, thereby facilitating the maintenance of one's own health in daily life.
subject: Articles
  topic: section
subject: Heart Rate Variability (HRV)
  topic: keywords
subject: Stress Detection
  topic: keywords
subject: 1D CNN
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: SWELL-KW Dataset
  topic: keywords
subject: ANOVA Feature Selection
  topic: keywords
subject: Time-Domain Features
  topic: keywords
subject: Multi-Class Classification
  topic: keywords
creator: Bhoorthi Ravali
creator: Mrs. B. Swarnalatha
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

ADVANCED CREDIT CARD FRAUD DETECTION USING ENSEMBLE LEARNING ALGORITHMS
type: item
description: The objective of this research is to improve the reliability and accuracy of financial system fraud detection through the use of advanced credit card fraud detection algorithms powered by ensemble learning algorithms. Credit card fraud has emerged as a significant concern for banks and other financial organizations due to the rapid expansion of online banking and digital payments. In order to more accurately evaluate transaction patterns and identify suspicious activity, the proposed paper investigates the use of ensemble methodologies, such as Random Forest, Gradient Boosting, AdaBoost, and XGBoost, in contrast to traditional machine learning models. Ensemble learning enhances the accuracy of predictions, reduces the number of false positives, and enhances the system's resilience to imbalanced datasets, all of which are common issues in fraud detection. In order to facilitate successful fraud detection, the paper prioritizes data preparation, feature selection, and model evaluation measures such as accuracy, precision, recall, and F1-score. The ultimate objective is to develop a fraud detection system that is both intelligent and scalable, thereby enabling institutions to mitigate losses and enhance the security of transactions in real time.
subject: Articles
  topic: section
subject: Credit Card Fraud Detection
  topic: keywords
subject: Ensemble Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Fraud Analytics
  topic: keywords
subject: Imbalanced Data
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Manchikatla Pavan
creator: Mr. S. Sateesh Reddy
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

PERSONALIZED DIGITAL COUPON ISSUANCE FOR ENHANCING RETAIL REVENUE IN SHOPPING MALLS
type: item
description: Big data and deep learning have recently gained prominence in a variety of industries, such as business administration and marketing, and have found new applications. Customer attrition management is a critical component of marketing that directly influences the efficiency of a company. This research demonstrated that real-time big data analysis may result in the provision of personalised discount coupons to clients with a high turnover rate. Consequently, consumer attrition decreased, while buy conversion rates increased. Initially, cluster analysis is employed to investigate two-dimensional consumer categories. Afterwards, we evaluate the clickstream data for each cohort in order to construct a real-time attrition prediction model. Sales that are customised to the preferences of each customer are generated and distributed through the use of insights such as these. The strategy's effectiveness was evaluated by factoring in revenue growth and conversion rates. The findings indicate that a hybrid model that integrates attrition estimation and recommendation systems outperforms conventional individual models in terms of consumer behaviour forecasting and engagement. By employing Support Vector Machine to objectively assess churn probability and purchase trends, online businesses can optimise their marketing expenditures, retain consumers, and boost sales.
subject: Articles
  topic: section
subject: Churn Management
  topic: keywords
subject: Coupon Generation
  topic: keywords
subject: Customer Segmentation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Purchasing Tendencies
  topic: keywords
subject: Marketing Cost
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Personalized Discount Coupon
  topic: keywords
creator: Gollapelli Meghana
creator: Dr. M. Srinivas
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

EVALUATING ENERGY-EFFICIENT DRIVING PATTERNS OF ELECTRIC BUSES AT STOPS
type: item
description: Eco-driving techniques can drastically lower the energy consumption and operating costs of electric buses, especially when approaching and departing stops that call for frequent acceleration and deceleration. In this Paper, we provide a methodology for evaluating the eco-driving capabilities of electric buses by gathering data from real-world scenarios and analysing driving behaviour. The collected data underwent pre-processing, which included data cleaning, parameter addition, and sample unification. By analysing a range of energy-related driving events, including acceleration patterns, braking behaviour, pedal operation, and the duration of economical speed, we were able to identify critical driving behaviour parameters that impact energy consumption. A multiple regression model and a machine learning recognition framework were developed to evaluate eco-driving efficiency and categorise driving behaviours as either eco-driving or non-eco-driving. Implementing eco-driving techniques, which enhanced energy recovery and decreased power consumption at bus stops, was made possible by optimising NSGA-II and driving modes. Significant energy savings, increased assessment and recognition accuracy, and robust support for fleet management and driver assistance systems were all demonstrated by the results. The proposed model promotes sustainable urban transportation by lowering operating costs, boosting energy efficiency, and improving intelligent electric bus driving management.
subject: Articles
  topic: section
subject: Electric buses
  topic: keywords
subject: Eco-driving
  topic: keywords
subject: Energy consumption
  topic: keywords
subject: Driving behavior analysis
  topic: keywords
subject: Bus stop operation
  topic: keywords
subject: Machine learning
  topic: keywords
subject: NSGA-II optimization
  topic: keywords
subject: Sustainable transportation
  topic: keywords
creator: Sriramoju Radhika
creator: Dr. P. Venkateshwarlu
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

FEATURE EXTRACTION AND CLASSIFICATION FOR SPAM DETECTION USING SUPERVISED ML
type: item
description: The objective of this research is to enhance the efficacy and precision of spam detection in digital communication systems by utilising supervised machine learning techniques for feature extraction and classification procedures. This paper examines large email and text message datasets for feature extraction using preprocessing techniques such as tokenization, stop-word deletion, stemming, and vectorization. A number of supervised machine learning techniques, including as Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, are compared in order to distinguish between spam and real messages. To measure how well these models work, we use metrics like F1-scores, recall, accuracy, and precision. In order to enhance classification accuracy and decrease false positives, the Paper stresses the significance of effective feature extraction methods as TF-IDF and Bag-of-Words. The proposed method allows trustworthy and secure communication by use of an adaptive spam detection framework that adjusts to actual changes in spam trends.
subject: Articles
  topic: section
subject: Spam Detection
  topic: keywords
subject: Supervised Machine Learning
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Text Classification
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Support Vector Machine (SVM)
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: Bag-of-Words
  topic: keywords
subject: Email Filtering
  topic: keywords
subject: Data Mining
  topic: keywords
creator: Padala Roja
creator: Mrs. A. Pavani
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

DEEP LEARNING MODELS FOR ANALYZING PUPILLARY DYNAMICS IN GENETIC DISORDER DETECTION
type: item
description: The primary objective of this initiative is to develop deep learning models that will investigate pupillary dynamics in the diagnosis of genetic diseases. Variations in pupil size, reflex speed, and light adaptation patterns, among other pupillary responses, are significant neurological and physiological markers that may be associated with underlying genetic disorders. The objective of this research is to analyze and comprehend pupillary behavior as captured by eye-tracking and imaging sensors by employing cutting-edge deep learning techniques, including CNNs, RNNs, and LSTM models. The proposed method seeks to improve the accuracy, efficiency, and early diagnosis of genetic abnormalities in comparison to current clinical assessment procedures by extracting complex spatial and temporal information from pupillary movement data. A non-invasive, cost-effective, and reliable diagnostic framework that integrates AI with neurological and ocular indicators can enable healthcare providers to benefit from predictive genetic analysis and individualized treatment plans.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Clinical decision support system
  topic: keywords
subject: Python
  topic: keywords
subject: Pupillometry
  topic: keywords
subject: Retinopathy
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: ELM
  topic: keywords
subject: pigmentosa
  topic: keywords
creator: Myakala Manasa
creator: Dr. E. Srikanth Reddy
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

ENHANCING PUBLIC SAFETY IN SMART CITIES USING DEEP LEARNING-BASED VIOLENCE DETECTION
type: item
description: This work illustrates the utilization of deep learning to detect violent incidents in smart cities, thereby improving public safety. Automated threat identification and real-time surveillance research are implemented by the technology. The proposed method employs sophisticated convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze video feeds from public surveillance cameras, accurately detecting violent incidents such as altercations, assaults, vandalism, and mass violence. The system's proficiency in distinguishing between violent and non-aggressive human behaviors across a variety of external conditions is attributed to its implementation of spatial and temporal feature extraction techniques. The model is trained using a vast array of annotated datasets. The goals are to improve the effectiveness of detection, decrease the number of false alarms, and facilitate the rapid response of law enforcement. The proposed method improves the efficiency of emergency response, reduces the need for human supervision, and enhances smart city monitoring by providing continuous updates on current events. The intelligence, safety, and security of urban areas can be substantially improved by this violence detection system, which employs deep learning.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Violence Detection
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Public Safety
  topic: keywords
subject: Surveillance Systems
  topic: keywords
subject: Convolutional Neural Networks (CNN)
  topic: keywords
subject: Human Activity Recognition
  topic: keywords
creator: Gaddamidhi Vaishika Goud
creator: Mrs. T. Mounika
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

MULTI CLASS TRAFFIC OBJECT DETECTION AND SCENE UNDERSTANDING USING SUPERVISED MODELS
type: item
description: The utilization of trained models to identify a variety of traffic objects and comprehend scenes is a critical component of intelligent transportation systems and autonomous driving technologies. This approach employs labeled datasets to instruct machine learning and deep learning algorithms on how to precisely identify and classify various road objects, including automobiles, pedestrians, traffic signs, bicycles, and lane markings, even in the presence of numerous moving vehicles. Supervised models, such as CNNs and advanced object recognition frameworks like YOLO, Faster R-CNN, and SSD, are capable of accurately and reliability identifying objects in real time. Scene understanding techniques encompass more than merely object recognition. Additionally, they examine the movement of traffic, the condition of the roadways, and the spatial relationships between objects, which aids in the comprehension of one's environment and the formulation of more informed decisions. Combining scene interpretation and multi-class detection enhances self-driving, traffic tracking, and road safety by providing vehicles with a more accurate perception of a diverse array of environmental conditions.
subject: Articles
  topic: section
subject: Difficult meteorological circumstances
  topic: keywords
subject: Intelligent vehicles
  topic: keywords
subject: Supervised learning
  topic: keywords
subject: Underlying visual features
  topic: keywords
subject: Categorization
  topic: keywords
creator: Janagam Rekha
creator: Mr. K. Nagendra Prasad
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

NETWORK SECURITY ENHANCEMENT THROUGH MACHINE LEARNING-BASED CYBER ATTACK DETECTION
type: item
description: The objective of this investigation is to identify and prevent harmful activities in contemporary communication networks by employing machine learning-based techniques to enhance network security and detect intrusions. Using supervised and unsupervised machine learning algorithms, including Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, the investigation examines network traffic patterns and identifies outliers that may be the result of cyber threats such as phishing, DOS attacks, malware intrusions, and unauthorized access attempts. The proposed method analyzes a variety of network metrics, including packet flow, connection duration, source and destination IP behavior, and protocol utilization, to facilitate the identification of both normal and anomalous activity. Traditional security systems that rely on signatures are significantly outperformed by machine learning-driven detection models in terms of real-time attack detection, false alarm rates, and threat detection speed. The paper underscores the critical nature of enhanced cyber security systems in order to ensure the security of private information, the reliability of networks, and the safety of online communication.
subject: Articles
  topic: section
subject: Network Security
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cyber Attack Detection
  topic: keywords
subject: Intrusion Detection System
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Neural Networks
  topic: keywords
creator: Gopannagiri Sneha
creator: Mrs. Y. Susheela
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

BLOOM FILTER-BASED CRYPTOGRAPHIC TECHNIQUES FOR CLOUD DATA SECURITY
type: item
description: This paper discusses Bloom Filter-based cryptographic algorithms that can be employed to securely store data and regulate access in cloud computing environments. These algorithms provide solutions that are effective, scalable, and privacy-preserving. The proposed approach minimizes the volume of data that must be stored and the work that must be performed on a computer by incorporating Bloom Filters and sophisticated cryptographic approaches. It also expedites the process of identifying unlawful data access, authenticating users, and verifying membership. In the cloud, bloom filters are an excellent tool for managing big data transactions, as they can rapidly process queries and provide a detailed description of large datasets. In order to safeguard data from cyber threats such as data breaches, hostile intrusions, and unauthorized modifications, the framework includes encryption algorithms, hash functions, and secure key management methods. The proposed method enhances security performance, reduces the number of false positives, and enhances operational efficiency in comparison to conventional cloud security practices, as demonstrated by tests. The paper demonstrated that Bloom Filter-based cryptographic models can enhance the dependability of data security in contemporary distributed computing settings and enhance the security of cloud storage systems.
subject: Articles
  topic: section
subject: Bloom Filter
  topic: keywords
subject: Cloud Data Security
  topic: keywords
subject: Cryptographic Techniques
  topic: keywords
subject: Secure Data Storage
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Access Control
  topic: keywords
subject: Hash Functions
  topic: keywords
subject: Encryption
  topic: keywords
creator: Deva Prathap Reddy
creator: Mr. R. Sagar
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

IMPROVING TRUST IN SOCIAL MEDIA PLATFORMS THROUGH ADVANCED MALICIOUS PROFILE DETECTION TECHNIQUES
type: item
description: This paper aims to enhance trust in social media platforms by identifying detrimental profiles in a more sophisticated manner through intelligent data analysis and machine learning. The proliferation of social media platforms has increased the likelihood of the occurrence of spam profiles, bots, false accounts, and cybercriminal activities. The veracity of information, the safety of the internet, and the privacy of users are all negatively impacted by these issues. The objective of this investigation is to devise a method for identifying fraudulent profiles by examining user behavior, profile details, posting habits, network connections, and content-related aspects. Machine learning and deep learning techniques, including Random Forest, Support Vector Machine, Decision Tree, and Neural Networks, are employed to facilitate the identification of genuine and fraudulent accounts. The framework employs anomaly detection and feature extraction to identify suspicious activities in real time. When we employed social media datasets for empirical testing, we observed enhanced platform security, fewer false hits, and improved detection performance. This research contributes to the safety of social media platforms by facilitating the identification and prevention of user misconduct.
subject: Articles
  topic: section
subject: Social Media Security
  topic: keywords
subject: Malicious Profile Detection
  topic: keywords
subject: Fake Accounts
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Spam Detection
  topic: keywords
creator: Puppala Harika
creator: Mrs. G. Aruna
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

MULTIMODAL ANALYSIS OF INTER-BRAIN AND BEHAVIORAL SYNCHRONY IN REMOTE LEARNING
type: item
description: This paper examines multimodal analysis of behavioral and inter-brain synchronization in remote learning environments to determine students' social and cognitive alignment when using digital platforms to participate. The paper integrates neurophysiological signals like EEG-based inter-brain coherence with behavioral indicators like eye movements, facial expressions, and interaction patterns to determine synchrony. Advanced signal processing and machine learning integrate diverse data sources. Next, learning outcomes, teamwork, and participation patterns are identified. Increased inter-brain synchrony improves academic performance, group attention, and communication even when people are absent. Synchrony-aware analytics improves virtual collaboration, adaptive training, and academic success, according to this paper. Additionally, it improves intelligent remote learning system design.
subject: Articles
  topic: section
subject: Inter-brain synchrony, Behavioral synchrony, Multimodal analysis, Remote learning, EEG, Social interaction, Machine learning, Collaborative learning
  topic: keywords
subject: Inter-brain synchrony
  topic: keywords
subject: Behavioral synchrony
  topic: keywords
subject: Multimodal analysis
  topic: keywords
subject: Remote learning
  topic: keywords
subject: EEG
  topic: keywords
subject: Social interaction
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Collaborative learning
  topic: keywords
creator: Mandala Aparna
creator: Mrs. P. Saritha
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

ATTENTION ENHANCED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC ANOMALY DETECTION
type: item
description: This paper investigates Attention Enhanced Convolutional Neural Networks (AE-CNNs) as a potential method for identifying traffic issues in scenarios where the highways are highly complex and are in a state of flux. By incorporating attention processes into conventional convolutional neural network topologies, the proposed approach prioritizes the most pertinent spatial and temporal data. This facilitates the identification of issues such as traffic congestion, unusual vehicle behavior, and collisions. By integrating channel and spatial attention modules, the model eliminates superfluous data and enhances feature representation. This renders it superior to conventional CNN-based methodologies. The system is more resilient, accurate, and sends fewer false warnings, as evidenced by experiments with various traffic datasets. The results indicate that AE-CNNs enhance real-time traffic monitoring and smart transportation, thereby enhancing the efficiency and safety of urban transportation.
subject: Articles
  topic: section
subject: Traffic Anomaly Detection
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Intelligent Transportation System
  topic: keywords
subject: Smart Traffic Monitoring
  topic: keywords
creator: Uppula Vaishnavi
creator: Mr. T. Raghupathi
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

DEEP NEURAL NETWORKS FOR SUSPICIOUS ACTIVITY RECOGNITION IN VIDEO SURVEILLANCE
type: item
description: The objective of this investigation is to enhance automated security monitoring systems by investigating the utilization of deep neural networks (DNNs) to identify suspicious activity in video surveillance. To accurately identify abnormal or potentially hazardous behaviors, the research employs advanced architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to extract temporal and spatial features from video streams. The proposed methodology effectively distinguishes between normal and suspicious behavior by incorporating pattern recognition, motion analysis, and feature learning. In order to address obstacles such as occlusion, variable lighting, and complex crowd dynamics, the investigation implements rigorous training methodologies and extensive, annotated datasets. Experimental results suggest that deep learning models outperform conventional machine learning methods in terms of adaptability, scalability, and accuracy. Subsequently, they are optimal for contemporary surveillance applications in public safety, transportation systems, and smart cities.
subject: Articles
  topic: section
subject: Deep Neural Networks (DNNs)
  topic: keywords
subject: Video Surveillance
  topic: keywords
subject: Suspicious Activity Recognition
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Convolutional Neural Networks (CNNs)
  topic: keywords
creator: Mushkam Suchithra
creator: Dr. D. Srinivas Reddy
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

AUTOMATED ATTENDANCE MANAGEMENT SYSTEM USING FACIAL RECOGNITION TECHNIQUES
type: item
description: This investigation introduces an automated method for monitoring the attendance of students or employees through the use of facial recognition technology, which improves accuracy, speed, and safety. Traditional methods, including biometric technologies and human roll calls, are error-prone, burdensome, and susceptible to fraudulent attendance. The proposed method employs computer vision and deep learning algorithms to identify and recognize individuals in real time using a camera. Upon the identification of a match within a database of known images, attendance is automatically documented. Convolutional Neural Networks (CNNs) are frequently employed by the system for the purposes of face detection, feature extraction, and classification. It ensures that individuals are not obligated to take any action, reduces administrative duties, and provides a dependable, contactless response, which is particularly advantageous in regions that are impacted by a pandemic. The attendance records can be stored online for swift access and analysis. The research demonstrates that attendance systems are more reliable, scalable, and efficient when facial recognition technology is implemented.
subject: Articles
  topic: section
subject: Automated Attendance System
  topic: keywords
subject: Facial Recognition
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Convolutional Neural Networks (CNN)
  topic: keywords
subject: Face Detection
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Biometric Authentication
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Attendance Tracking
  topic: keywords
creator: Rayyan Ur Rahnan
creator: Dr. P. Venkateshwarlu
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

A HYBRID ML FRAMEWORK FOR PERSONALITY-BASED CUSTOMER SERVICE OPTIMIZATION
type: item
description: This research introduces a hybrid machine learning framework that optimizes customer service by incorporating personality. The goal is to enhance service efficiency and consumer satisfaction by meticulously examining their behavior. The framework employs machine learning algorithms, natural language processing, and personality prediction techniques to ascertain the personality characteristics of a client by analyzing their communication patterns, preferences, and previous interactions. Clients are categorized according to their personality traits in the recommended methodology. This allows businesses to offer them more personalized responses, suggestions, and methods of support. The hybrid approach combines predictive analytics and categorization to enhance consumer behavior prediction and facilitate decision-making. The framework enhances the overall quality of service in digital customer support environments, reduces response times, and increases customer engagement, as demonstrated by an experiment. The research also illustrates the importance of AI-based personalization in modern CRM systems and provides a practical solution for improving customer service in a variety of sectors.
subject: Articles
  topic: section
subject: Hybrid Machine Learning
  topic: keywords
subject: Personality Prediction
  topic: keywords
subject: Customer Service Optimization
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Customer Behavior Analysis
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Personalized Support
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Jakkula Ashritha
creator: Mr. K. Nagendra Prasad
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

INTEGRATING BIOINFORMATICS AND DEEP LEARNING FOR DRUG SIDE EFFECT PREDICTION
type: item
description: This research investigates the integration of deep learning techniques and bioinformatics to enhance the precision and efficacy of pharmacological adverse effect forecasts. The objective of this investigation is to identify intricate patterns and concealed connections that result in adverse drug reactions by integrating advanced deep learning algorithms with bioinformatics data, including genetic information, protein interactions, and drug-target networks. In contrast to conventional statistical methodologies, the methodology utilizes a wide range of datasets to improve the accuracy of its forecasts. The objective of this research is to enhance the precision and reliability of side effect predictions, reduce risks in clinical trials, and advance personalized medicine by enhancing early pharmaceutical safety evaluation.
subject: Articles
  topic: section
subject: Bioinformatics
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Drug Side Effect Prediction
  topic: keywords
subject: Adverse Drug Reactions (ADR)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Multi-omics Data
  topic: keywords
subject: Drug–Target Interaction
  topic: keywords
subject: Pharmacovigilance
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Chegonda Sri Keerthana
creator: Mrs. Y. Susheela
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

PREDICTIVE MODELING OF CUSTOMER PURCHASE BEHAVIOR USING RETAIL DATA MINING METHODS
type: item
description: This research examines the process of predicting consumer behavior by utilizing retail data mining to evaluate a substantial quantity of transactional and customer data in order to identify patterns that can be used to anticipate future purchases. This project employs a variety of sophisticated machine learning techniques, such as clustering, classification, and association rule mining, to accurately classify clients, ascertain their preferences, and forecast purchasing trends. The research demonstrates the potential of behavioral, demographic, and historical sales data to enhance targeted marketing, assist in strategic decision-making, and enhance inventory management. By utilizing predictive and educated data, this method enables retailers to increase consumer engagement and generate additional revenue.
subject: Articles
  topic: section
subject: Demand-based pricing
  topic: keywords
subject: Dynamic pricing
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Contextual bandits
  topic: keywords
subject: Price elasticity modeling
  topic: keywords
subject: E-commerce analytics
  topic: keywords
subject: Real-time pricing systems
  topic: keywords
creator: Mudrakola Raghuram
creator: Dr. N. Chandramouli
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-10
publication date: 2026-06-10

PROFITABILITY AND RETURN ANALYSIS THROUGH GROWTH STRATEGIES AT UNION BANK OF INDIA
type: item
description: This paper evaluates Union Bank of India's profitability and return by assessing recent expansion initiatives. It analyzes how strategy goals like credit portfolio expansion, digital banking, and cost reduction affect the bank's finances. The paper examines net profit margins, return on assets, and return on equity to assess the organization's efficiency and profitability. It investigates how market, competition, and legal changes affect bank profitability. Development activities linked to financial results improve performance in an environmentally sustainable way, according to the research. The paper's list of finance and business administration's pros and cons can help strategic decision-makers. The paper recommends developing new financial solutions and focusing on customer preferences to boost revenue.
subject: Articles
  topic: section
subject: Profitability Analysis
  topic: keywords
subject: Return Analysis
  topic: keywords
subject: Union Bank of India
  topic: keywords
subject: Growth Strategies
  topic: keywords
subject: Financial Performance
  topic: keywords
subject: Return on Assets (ROA)
  topic: keywords
subject: Return on Equity (ROE).
  topic: keywords
creator: Rellacharla Prameela
creator: Dr. P. Vijaya Lakshmi
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-03
publication date: 2026-06-03

COMPENSATION STRUCTURES AT HCL TECHNOLOGIES
type: item
description: This paper seeks to shed light on the impact of compensation policies on employee motivation, retention, and organizational performance by analyzing the pay structures at HCL Technologies, a prominent IT service provider in India. Examining components such as base pay, performance-based incentives, bonuses, benefits, and long-term rewards, the paper determines how effectively HCL's compensation structure complies with industry standards and meets employee expectations. In order to promote employee loyalty and increase productivity, the paper examines how competitive and transparent compensation systems work. Based on the results, it seems that a well-planned compensation plan is essential in the competitive IT sector for attracting and retaining top talent, maintaining employee engagement, and driving organizational growth. Researchers hope that HR experts and business leaders will use the paper's findings to better understand how to structure compensation packages to boost morale and productivity.
subject: Articles
  topic: section
subject: Compensation Structure
  topic: keywords
subject: Employee Motivation
  topic: keywords
subject: Talent Retention
  topic: keywords
subject: HCL Technologies
  topic: keywords
subject: Remuneration Policies
  topic: keywords
subject: Performance-Based Incentives
  topic: keywords
subject: Employee Benefits
  topic: keywords
creator: Priya Manasa
creator: Mr. Siva Rama Krishna Vuppala
rights: Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-06-03
publication date: 2026-06-03