INTERNATIONAL JOURNAL OF ADVANCED RESEARCH AND REVIEW (IJARR)
Welcome to IJARR The International Journal of Advanced Research and Review (IJARR) is a peer-reviewed, open-access international scientific journal dedicated to the rapid monthly publication of high-quality, original research articles, case reports, and review papers across all disciplines. With an internationally recognized editorial board, IJARR is committed to producing research content of the highest standard. Our goal is to foster long-lasting, mutually beneficial relationships with our authors, providing comprehensive support throughout the entire publishing process—from submission to post-publication. Editor- In-Chief : Dr. Rajeev Mehra Title proper : INTERNATIONAL JOURNAL OF ADVANCED RESEARCH AND REVIEW (IJARR) Issn : 2455-7277 Subject : Research Sciences Publisher : IJARR Publishers Dates of publication : 2016 Frequency : Monthly Language : English Country : India Medium : Online Indexed by : CROSSREF Indexed by : ROAD Indexed by : THE KEEPERS Title DOI of the Journal : http://doi.org/10.70914/issn.2347-2251
Enhancing Insider Threat Detection with Machine Learning Techniques
The compromise of confidential information and company assets by insiders is a major concern for every business. For these dangers to be detected, strong ML methods are required, ones that can handle skewed and complicated datasets. This research examines the efficacy of several machine learning models on the renowned CERT dataset. These models include Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naïve Bayes, Adaboost, and XGBoost. Methods like SMOTE bring attention to the significance of a balanced dataset by fixing problems caused by data imbalance. With an accuracy of 97.5%, Random Forest and Adaboost proved to be the most successful in detecting insider threats, according to the data. Improved organizational security strategies are possible because to this research's contributions to insider threat detection approaches and structured examination of model performance.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Implementation of Hand Gesture Based Virtual Mouse
Since the invention of computers, there has been a dramatic shift in the method of creating contact between computers and people. The mouse is one example of a groundbreaking invention in HCI. The wireless or Bluetooth mouse is only one example of how these technologies continue to rely on supplementary hardware. For instance, a Bluetooth mouse may need a connecting dongle in addition to batteries. Such gadgets may be less user-friendly due to the need of these additional components. In this study, we provide a virtual mouse technology that solves these problems. Computer vision and hand gestures are the backbone of the proposed system, which is based on HCI concepts.
A Study on Comparative Analysis of Machine Learning Models For Melon Growth Prediction
Problems with sustainable farming have been intensified in recent years due to the substantial influence of climate change and environmental degradation on agricultural production. Precise growth prediction models are essential for ensuring appropriate production methods of melons (Cucumis melo L.), a highly valuable crop that is especially susceptible to environmental influences
A Review of Generative AI in Recommendation Systems
The development of Gen AI has been largely enabled by traditional machine learning methods, which have allowed for the creation of increasingly complex models. Gen AI's use in recommendation systems is contrasted with that of more conventional AI techniques, as well as the applications of both types of AI in other domains, in this review. By fixing the problems with traditional machine learning methods, generative AI has revolutionized recommendation systems. Focusing on GANs and VAEs, this paper analyzes the use of Gen AI and traditional AI in RS across different domains. Low data, cold start, and suggestion variety are some of the issues that these generative models highlight as strengths and weaknesses. Further study might be hindered by them as well. This research summarizes current efforts to illustrate how Gen AI improves RS performance, follows important trends, and answers pertinent new questions about ethics and dependability. Hybrid techniques are highlighted by the findings, which bode well for future advancements of the effective and versatile RSs. The report ends by stating that further research should be conducted.
Partnerships for Real-Time Threat Intelligence to Strengthen Cyber Defenses
Cyber Threat Intelligence (CTI) is becoming more important in the ever-changing world of cyber security and cyber warfare as a means of collecting and disseminating up-to-date information on threats and their patterns. Problems with scalability, trust, and data quality plague existing CTI sharing solutions (such as ISACs and automated STIX/TAXII platforms)
Digital Forensics and Artificial Intelligence: Revolutionizing Crime Scene Investigations
Enhancing CSI, digital forensics with AI increases efficiency, accuracy, execution throughput, and physical evidence inclusion in trials
Using Convolutional Neural Networks for Classification of Satellite Images
The project uses convolutional neural network (CNN) satellite picture categorization to search the internet for photos of clouds, deserts, greenery, and water. Kaggle used data augmentation methods including rescaling, zooming, and flipping to preprocess the many satellite photos, making the model stronger.
Consumption of technical threat intel indicators
More and more, our daily lives rely on the enabling function of IT systems. Consistent with this, the increasing frequency and severity of cyberattacks is a major concern. Both businesses and governments must now take measures to protect themselves against cybercriminals. It is becoming more and more difficult to determine the right reaction to cyber-attacks, despite the fact that proactive defense and quick response are vital for IT security experts.
Using Deep Convolutional Neural Networks with Transfer Learning for Reptile and Amphibian Classification
This paper presents a new approach to amphibian and reptile automated classification utilizing deep convolutional neural networks (CNNs) and supervised learning. Drawing on our knowledge of the ecological importance of these two vertebrate groups and the limitations of traditional classification methods, we use deep learning to train a MobileNetV2 model that can accurately identify species. We use transfer learning to optimize a pre-trained CNN on a massive collection of reptile and amphibian pictures, allowing us to overcome the limitations of small-scale datasets. Because of its excellent extraction efficiency, the model is able to generalize well to many other species. In addition, the study delves into the significance of image augmentation techniques for enhancing model performance, particularly in cases when labeled data is few.
Optimized Structural Analysis and Design of a Long-Span Pre-Engineered Steel Warehouse Using STAAD.Pro
tructural design is aimed to design a structure that full fills its intended purpose during its intended life span and be adequately safe in terms of strength, stability, and structural integrity, serviceability in terms of stiffness, durability, etc., and be economically viable, aesthetically pleasing, and environment friendly. This paper presents the studies on the analysis and design of the steel warehouse structure. The optimum design of the structure is carried out using finite element software STAAD Pro. The analysis of the structure is carried out for suitable steel sections with different load carrying capacity. The steel quantity required for the structure is calculated. Finally, along with material optimization, techno-economical design to achieve the reliable performance of the warehouse structure is carried out.
Pre – Engineered Buildings (Ware House),
INTEGRATED STABILITY AND STRUCTURAL PERFORMANCE EVALUATION OF AN RCC GRAVITY DAM UNDER MULTI-LOAD CONDITIONS
Gravity dams are among the most reliable hydraulic structures due to their ability to resist external forces primarily through self-weight. This project presents a comprehensive structural and stability analysis of a Reinforced Cement Concrete (RCC) gravity dam considering critical loading conditions such as water pressure, uplift pressure, silt pressure, and seismic forces. Detailed site investigation data, hydrological parameters, and foundation characteristics were incorporated to ensure realistic design assumptions. Stability checks against sliding, overturning, and foundation overstress were performed using analytical methods in accordance with standard dam design guidelines. Additionally, structural modeling and verification were carried out using STAAD
HYDROSTATIC LOAD RESPONSE ANALYSIS OF A RECTANGULAR REINFORCED CONCRETE WATER TANK
Water storage structures play a vital role in municipal, industrial, and domestic water supply systems. The present project focuses on the analysis and design of a rectangular reinforced cement concrete (RCC) water tank using STAAD Pro software, in accordance with relevant Indian Standard codes such as IS 3370 and IS 456:2000.
FINITE ELEMENT–BASED STRUCTURAL ANALYSIS OF AN RCC BOX CULVERT UNDER COMPLEX LOADING CONDITIONS USING STAAD.PRO
Box culverts are widely used in highway and railway embankments to facilitate safe cross drainage of streams, canals, and storm water without obstructing natural flow. Due to their rigid frame action, monolithic behavior, and economic advantages, reinforced cement concrete (RCC) box culverts are commonly adopted for small to medium span crossings. This project focuses on the structural analysis and design of a single-cell RCC box culvert using STAAD.Pro software
STUDENT ATTENDANCE MANAGEMENT SYSTEM USING RFID TECHNOLOGY
The Student Attendance Management System using RFID Technology is an automated and contactless solution designed to replace traditional manual roll-calling methods. Conventional attendance systems are time-consuming, error-prone, and susceptible to proxy attendance. To overcome these limitations, the proposed system integrates an RFID-based identification mechanism with an Arduino Uno microcontroller to ensure fast, accurate, and reliable attendance tracking
DESIGN AND IMPLEMENTATION OF AN AUTOMATED IOT BASED FISH FEEDING SYSTEM
This paper presents a smart and efficient solution for automating aquarium maintenance using Internet of Things (IoT) technology. Traditional fish care requires regular feeding and periodic water management, which are often inconsistent due to human negligence or busy schedules
DESIGN AND IMPLEMENTATION OF A CLOUD CONNECTED HOME AUTOMATION SYSTEM USING NODEMCU
This project presents the design and implementation of a cloud-connected home automation system using the NodeMCU (ESP8266) microcontroller. The proposed system provides an intelligent, internet-enabled solution for remotely monitoring and controlling household electrical appliances through a Wi-Fi-based embedded platform. The NodeMCU, which integrates a 32-bit processor with built-in Wi-Fi capability, serves as the core controller enabling seamless communication between physical devices and cloud services.
DEVELOPMENT OF AN IOT ENABLED WIRELESS SMART NOTICE BOARD USING NODEMCU
Development of an IoT Enabled Wireless Smart Notice Board Using NodeMCU presents a modern communication solution designed to replace traditional paper- based notice boards with a real-time, internet-connected digital display system. The system is built around the NodeMCU (ESP8266) microcontroller, which provides integrated Wi-Fi capability for seamless wireless communication
EXPERIMENTAL ANALYSIS AND IMPLEMENTATION OF A SINGLE AXIS SOLAR TRACKING SYSTEM USING ARDUINO
Experimental Analysis and Implementation of a Single Axis Solar Tracking System Using Arduino shows the design, development, and performance evaluation of an automated solar tracking mechanism aimed at enhancing photovoltaic (PV) panel efficiency. Conventional fixed solar panels experience reduced energy output due to the continuous movement of the sun across the sky.
IMPLEMENTATION OF AN ARDUINO BASED SMART PARKING MANAGEMENT SYSTEM WITH REAL TIME AVAILABILITY MONITORING
Implementation of an Arduino Based Smart Parking Management System with Real-Time Availability Monitoring presents an automated solution to address the growing challenges of urban parking congestion. With the rapid increase in vehicle ownership, traditional manual parking systems often lead to inefficiencies, increased waiting time, and traffic congestion. This system utilizes an Arduino Uno microcontroller as the central processing unit to monitor parking slot occupancy and control vehicle entry through an automated gate mechanism. Five IR sensors are deployed—four for individual slot detection and one at the entry gate—to continuously monitor vehicle presence.
PRECICION COLOUR SENSING AND CLASSIFICATION SYSTEM FOR INDUSTRIAL AUTOMATION
Precision Colour Sensing and Classification System for Industrial Automation presents a real-time embedded solution for accurate color detection and classification in automated environments. The system is built around the Arduino Uno microcontroller and the TCS3200 (TCS230) color sensor, which operates on a light-to-frequency conversion principle to measure the intensity of Red, Green, and Blue (RGB) components reflected from an object
Vermiculture as an Eco-Friendly Approach for Organic Waste Management and Soil Health Improvement
The rapid increase in organic waste generation due to urbanization, agricultural activities, and population growth poses serious environmental challenges worldwide. Conventional waste disposal methods such as landfilling and incineration are associated with environmental pollution, greenhouse gas emissions, and loss of valuable nutrients. Vermiculture, the cultivation of earthworms for organic waste decomposition, has emerged as an eco-friendly and sustainable alternative for organic waste management. This process converts organic waste into vermicompost, a nutrient-rich organic fertilizer that enhances soil fertility, microbial activity, and crop productivity. The present article provides a comprehensive review of vermiculture principles, earthworm species used, vermicomposting processes, and their role in waste recycling and soil health improvement. Experimental and comparative data on nutrient content, waste reduction efficiency, and soil quality indicators are analyzed. The results demonstrate that vermiculture significantly reduces organic waste volume while producing high-quality compost that improves soil structure and plant growth. The study highlights vermiculture as a low-cost, sustainable, and environmentally friendly technology suitable for agricultural and urban waste management.
Insect Pest Identification Using Convolutional Neural Network (CNN) Techniques for Precision Agriculture
Accurate and timely identification of insect pests is a critical component of effective pest management in agricultural systems. Traditional pest identification methods rely heavily on expert knowledge and manual inspection, which are time-consuming, labor-intensive, and prone to human error. Recent advances in artificial intelligence, particularly deep learning techniques such as Convolutional Neural Networks (CNNs), have shown great potential in automating insect pest identification with high accuracy. This study explores the application of CNN-based image classification models for identifying insect pest species in agroecosystems. High-resolution images of major agricultural pests were used to train and validate CNN architectures for feature extraction and classification. The results demonstrate that CNN models can effectively distinguish insect pests based on morphological characteristics such as shape, color, texture, and wing patterns. The integration of CNN-based pest identification systems can support real-time decision-making, reduce dependency on chemical pesticides, and enhance precision agriculture practices. This approach represents a promising tool for sustainable pest management and digital agriculture.
PREDICTING STOCK MARKET TRENDS WITH DATA SCIENCE
The project on Predicting Stock Market Trends with Data Science focuses on developing a predictive model that uses historical stock data, technical indicators, and machine learning algorithms to forecast stock price movements and market trends. The stock market is inherently volatile, and investors face challenges in making informed decisions due to the complexity and unpredictability of price fluctuations. Traditional methods rely on manual analysis or basic statistical tools, which are often insufficient for accurate forecasting. This project integrates data science techniques, including data preprocessing, feature extraction, and model training, to analyse past stock behaviour and identify patterns that can predict future trends. Models such as linear regression, decision trees, random forests, and neural networks are utilized to capture both linear and nonlinear relationships in the data. The system also implements performance evaluation metrics, such as mean squared error (MSE) and accuracy, to validate prediction models. By leveraging real-time data feeds, the system provides timely insights for investors and traders, enabling data-driven decision-making. Security and privacy are considered in storing financial data, while scalability is ensured for handling large datasets. This project demonstrates the integration of data science, machine learning, and stock market analysis to create a predictive system that can assist investors, improve trading strategies, and enhance understanding of financial market dynamics.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0
The Security in Online Hotel Booking System project focuses on ensuring the confidentiality, integrity, and availability of user data in online hotel booking applications. With the rapid growth of digital booking platforms, sensitive information such as personal details, payment data, contact information, and booking history is frequently transmitted and stored online, which makes these systems vulnerable to cyber-attacks, unauthorized access, and data breaches. Existing hotel booking systems often lack robust security measures, which can lead to financial fraud, identity theft, and loss of user trust. This project implements multiple security features including data encryption, secure authentication mechanisms, role-based access control, secure payment gateways, and activity logging to monitor suspicious behaviour. By adopting modern web security protocols and best practices, the system protects sensitive user information during storage, processing, and transmission, reducing the risk of data compromise. Additionally, the system provides secure access for hotel staff and administrators, ensuring that only authorized personnel can modify booking details or view sensitive data. Security monitoring and alert mechanisms detect potential threats and respond in real time, improving overall system reliability. The system also educates users on safe online practices, ensuring awareness and reducing risk exposure.
RESUME SCREENING SYSTEM REVOLUTIONIZING TALENT ACQUISITION THROUGH DATASCIENCE
In today’s competitive job market, organizations receive thousands of resumes for a single job role, making manual resume screening time-consuming, costly, and prone to human bias. To overcome these challenges, this paper proposes an intelligent Resume Screening System powered by Data Science and Machine Learning techniques. The system automatically analyzes, filters, and ranks resumes based on job requirements such as skills, experience, and qualifications. Natural Language Processing (NLP) techniques are used to extract relevant information from resumes, while machine learning algorithms help in matching candidates to suitable job profiles. This approach improves recruitment efficiency, reduces hiring time, and ensures fair candidate evaluation. The proposed system revolutionizes talent acquisition by enabling faster, accurate, and unbiased resume screening.
Kandepi N S V Venkata Sri Sai LakshmiAnuhya
Restaurant Review Website: Elevating Culinary Exploration
The "Restaurant Review Website: Elevating Culinary Exploration" web-based system was created to help patrons, restaurant owners, and administrators by offering a dependable and data-driven platform for well-informed eating choices. Only authenticated users can access tailored features thanks to the system's secure login and registration capabilities. Users may browse restaurants, rate them, leave reviews, and explore dining options based on geography, cuisine, and feedback trends using an easy-to-use interface. To enhance the quality of their services, restaurant operators can monitor client feedback, update menus, and manage establishment profiles. Administrators supervise platform operations, control user behaviour, and guarantee the dependability and transparency of material. The system incorporates a centralized MySQL database to provide safe and scalable storage of restaurant and user-generated data.
REAL TIME SOCIAL MEDIA ANALYTICS USING AWS
Social media platforms generate massive volumes of real-time data that contain valuable insights for businesses and organizations. Traditional analytics systems struggle to process this data efficiently due to scalability and latency issues. This project proposes a Real-Time Social Media Analytics System using Amazon Web Services (AWS). The system collects streaming social media data and processes it in real time using cloud-based services. AWS provides scalable infrastructure to handle high data velocity and volume. Machine learning techniques are applied to analyse sentiments, trends, and engagement metrics. Real-time dashboards visualize insights for decision-makers. Automated alerts help detect viral events and negative sentiments. The system ensures high availability and fault tolerance. This project demonstrates how AWS can support real-time big data analytics effectively.
OPTIMIZING THE DELIVERY ROUTES IN ONLINE FOOD DELIVERY PLATFORMS
The emergence of internet meal delivery services has completely changed how people eat. However, optimal supply channels are crucial to these platforms’ effectiveness. With an emphasis on Angular as the frontend framework, this research investigates the complex task of delivery route optimization in online meal delivery systems. The study improves the effectiveness of food delivery operations by assessing current approaches, utilizing cutting-edge algorithms, and incorporating real-time data. In order to improve online meal delivery services, the project carefully investigates route optimization techniques with an emphasis on user experience, delivery time reduction, and cost effectiveness. Although consumers have benefited greatly from the quick expansion of online meal delivery services, there are now difficulties in optimizing delivery routes to guarantee effectiveness and customer happiness. In order to enhance the delivery process on online meal delivery platforms, this study investigates a number of optimization strategies and algorithms.
ONLINE E-DIARY: DIGITAL DIARY MANAGEMENT FOR MODERN AGRIBUSINESS WITH FULL-STACK EXPERTISE
The Online E-Diary project is designed to provide digital diary management solutions for modern agribusinesses, enabling farmers, agronomists, and agribusiness managers to record, track, and analyse agricultural activities efficiently. Traditional record-keeping in agriculture relies on manual diaries, spreadsheets, or fragmented notes, which are prone to errors, loss, and inefficiency. This project proposes a full-stack web-based digital diary that integrates user-friendly interfaces, secure storage, and analytical features to monitor crop activities, inputs, harvest schedules, and business operations. By leveraging full-stack development technologies such as HTML, CSS, JavaScript, Node.js, and database management systems, the system offers real-time updates, multi-user collaboration, and access control for different stakeholders. Users can log daily activities, input crop health data, track inventory, and generate reports for better decision-making. Security measures such as authentication, role-based access, and encrypted data storage ensure the safety of sensitive information. Additionally, the system provides notifications, reminders, and analytics to optimize farming practices and operational efficiency. This digital solution reduces administrative burden, enhances record accuracy, and allows agribusiness stakeholders to make informed decisions quickly. By integrating modern web technologies, full-stack expertise, and agricultural knowledge, the Online E-Diary serves as a practical, efficient, and secure platform for streamlining agribusiness management in the digital age.
Money Manager: Intelligent Financial Management with Full-Stack Expertise
In the fast-paced world of finance, managing personal finances effectively is crucial. This project presents an advanced Money Manager application developed using a Full-Stack approach, providing users with comprehensive financial management tools. Leveraging modern front-end technologies like React and dynamic back-end frameworks like Node.js, Express.js, and MongoDB, this Money Manager empowers users to track expenses, set budgets, and plan investments seamlessly. The project emphasizes intuitive user interfaces, robust back-end algorithms, secure data storage, and real-time analytics, ensuring users have a holistic view of their finances. By integrating smart categorization, goal tracking, secure cloud storage, and insightful reporting, this Money Manager guarantees users intelligent and personalized financial management experiences.