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
Design and Analysis of Multi-Protocol Conversion Unit for SPI, I2C and UART
The rapid proliferation of heterogeneous embedded systems has created an acute demand for intelligent hardware that can seamlessly bridge fundamentally different serial communication protocols. Modern Internet of Things (IoT) nodes, industrial automation platforms, automotive electronics, and medical instrumentation routinely require simultaneous interfacing with sensors, actuators, and processing elements that each expose a different communication standard. The absence of a unified, hardware-level protocol conversion fabric forces system designers to resort to inefficient software-driven translation layers that introduce latency, consume excessive processing bandwidth, and fail to scale with increasing data throughput requirements. This thesis presents the complete design, implementation, and analysis of a Multi-Protocol Conversion Unit (MPCU) capable of performing bidirectional, low-latency conversion between three of the most widely deployed synchronous and asynchronous serial communication protocols: Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C), and Universal Asynchronous Receiver-Transmitter (UART). The proposed architecture is described entirely in synthesizable Register-Transfer Level (RTL) Verilog, enabling direct implementation on commercially available Field-Programmable Gate Arrays (FPGAs) or integration into custom Application-Specific Integrated Circuit (ASIC) flows The MPCU comprises five primary functional blocks: a configurable SPI master controller supporting all four SPI modes (CPOL/CPHA combinations), a standard- and fast-mode I2C master controller with 7-bit addressing and ACK/NACK detection, a UART transceiver with 16x oversampling, selectable baud rates up to 115,200 bps, and configurable parity, an eight-entry per-channel FIFO buffering mechanism, and a centralized protocol bridge arbitration and conversion state machine. The entire design is parameterized for flexibility and governed by a lightweight memory-mapped configuration register interface.
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Low Power Crypto-chip Design for IoT Applications
Cryptography is fundamental to ensuring data security and integrity in modern digital communications. While numerous algorithms have been developed for data encoding and decoding, the Advanced Encryption Standard (AES) emerged as a robust solution for securing large and confidential datasets where existing algorithms proved inadequate. Initially designed to protect highly sensitive information, AES has become widely adopted in networking applications as the standard for data protection. This paper presents a low-power crypto-chip design for IoT applications implementing the AES algorithm using Verilog HDL. AES operates on 16-byte blocks with variable key sizes ranging from 128 to 256 bits. The implementation leverages Verilog's advantages over standard VHDL, offering significantly reduced operation time and propagation delay for encoding and decoding operations. Unlike its predecessor DES (Data Encryption Standard), which suffered from a fixed 56-bit key size limitation, AES provides flexibility through variable key sizes, enhancing security while maintaining computational efficiency. The proposed design achieves 4635 LUTs with a delay of 6.548 ns, demonstrating the feasibility of hardware-accelerated cryptographic operations for resource-constrained IoT devices.
E-Vote Secure: An AI-Powered Electronic Voting Machine Using Face Recognition and Raspberry Pi
Traditional paper-based voting systems are plagued by issues of booth capturing, impersonation, ballot stuffing, and manual counting errors. This paper presents E-VoteSecure, a novel Electronic Voting Machine (EVM) that integrates Artificial Intelligence-based facial recognition with Raspberry Pi hardware to create a secure, tamper-proof, and transparent democratic process. The proposed system employs a K-Nearest Neighbors (KNN) machine learning algorithm trained using OpenCV and Haar Cascade classifiers to perform real-time biometric voter authentication. The web-based application is built on Python Flask with a SQLite backend featuring SHA-256 password hashing and a one-vote-per-person constraint enforced at the database level. A multi-tier role-based system accommodates voters, candidates, and election administrators with dedicated dashboards, approval workflows, and real-time result visualization. Experimental results demonstrate high face recognition accuracy, effective duplicate vote prevention, and a streamlined end-to-end voting workflow suitable for deployment in local elections, institutional polls, and remote constituencies.
LoRa Based Low-Cost Real-Time Air Quality Monitoring System
The use of LoRa technology allows for the implementation of a cost-effective system for monitoring the interior and outdoor air quality in real-time, with factors such as temperature, humidity, PM 2.5, and formaldehyde being measured. A star topology is used in the system's architecture, with sensor nodes linked to a routing node that is linked to a gateway node. A microcontroller unit is coupled with inexpensive, off-the-shelf sensors to form the sensor nodes. Again, the routing node is built using a low-cost microcontroller (like Raspberry Pi) and receives sensor data using a LoRa hat module SX1268, which transmits the data. The gateway device has a server and a time-series database installed for processing, storing, and visualizing data. The transmission range that may be accomplished with LoRa technology is around 3 km with no packet loss and 4 km with 20% packet loss.
Vehicle Accident Alerting and Monitoring with IOT-based Data Analytics
An IoT-based smart accident reporting and environmental monitoring system is being designed and implemented in this project. To identify vehicle accidents, the system uses accelerometer data that is higher than a certain threshold; to identify fires, it uses data from a flame sensor. After an event is detected, the system uses GPS to pinpoint the spot and sends the information to an emergency response team using a GSM module. In addition, the system keeps tabs on variables like humidity, temperature, alcohol, smoke, and carbon monoxide levels using the right sensors. These data points are received by the IoT platform, which may then analyze and display them in real- time. Potentially applicable in smart transportation systems, this integrated approach enhances safety via facilitating rapid accident reaction and environmental monitoring. Topics covered include thingspeak, sensors, IoT, GPS, data monitoring, ESP8266, and hazard warnings.
Despite touchscreens' monopoly on portable devices in the modern digital world, they remain prohibitively pricey for PC workstation systems. However, a tacit human-computer junction, most notably a mouse controlled by hand gestures, may bridge this gap.This study proposes a novel smart-mouse program that uses a camera to detect and understand user hand gestures for system control.In order to do actions like as streeling, left- clicking, right-clicking, and cursor helm without requiring a physical device, the system records and processes hand gestures using OpenCV and advanced machine learning simulations. List of In this context, "terms" refer to things like object tracking, OpenCV, Mediapipe, palm detection model, hand gesture recognition, and human-computer interface.
IOT-based Design and Simulation of Smart Greenhouse Monitoring and Control System
In recent years, "smart agriculture" has emerged as a promising strategy for enhancing the efficiency and longevity of contemporary farming. This article details the planning and modeling of an Internet of Things (IoT) smart greenhouse management and monitoring system using MATLAB. In real time, the system tracks five important environmental variables using modelled sensor readings: soil moisture, humidity, light intensity, CO₂ concentration, and temperature. For the purpose of environmental monitoring and management, we have established precise cutoff values for each metric. When a value above its threshold, an alarm is sent out and appropriate procedures, such as the activation of fans, water pumps, and artificial lighting, are immediately initiated. The system ensured the correctness of the projected findings under various environmental circumstances by testing this configuration using a dataset of 100 time-series samples. The simulation found that temperature was the most unpredictable factor, leading to 58 warnings. Then came 52 warnings about soil moisture and 46 about humidity. Only 29 and 17 alarms, respectively, were generated by CO₂ levels and light intensity, which were closer to their ideal limits. In response to any alarm, the system automatically handles the inappropriate circumstances based on sensor input. The model made use of graphs and pie charts to examine the system. This study lays the groundwork for an Internet of Things (IoT) smart greenhouse system that may boost efficiency and longevity in real time. Assisting small farmers and promoting sustainable agriculture is the primary goal of this project. Smart farming, automation, environmental monitoring, control systems, sensor data, the internet of things, and MATLAB simulation are all related terms.
VLSI Implementation of Image Encryption and Decryption Using Reversible Logic Gates
As a crucial strategy for low power design and quantum computing, reversible logic synthesis and testing is an intriguing field of study. A wide variety of fields may benefit from reversible calculations, including quantum computing, nanotechnology, bio-information, digital signal processing, and many more. To protect sensitive information from prying eyes, a cryptography system is essential for all of these uses. One of the main issues with well-cured cryptography algorithms is their high power and area requirements. To get around these issues, this paper suggests a Reversible Logic Gates Cryptography Design (RLGCD). Architectures for encryption and decryption are both designed using RLGCD. To encrypt and decrypt data, the key is generated using a Linear Feedback Shift Register. The Least Significant Bit (LSB) technique is used for data watermarking in order to further enhance data security. It assesses the RLGCD architecture's FPGA performance. The performance of RLGCD architecture is much better than that of other traditional systems.
Reversible Logic Gate Cryptography Design (RLGCD),
AI-Powered Edge Vision Traffic Management Platform for Real Time Monitoring and Congestion Reduction
Drowsiness and fatigue are among the top factors that are considered to contribute to car accidents, causing 1.3 million deaths per year. By using facial landmark identification, the Advanced Drowsiness identification System is able to accomplish its goal of lowering the total number of road accidents caused by various forms of driver weariness and falling asleep behind the wheel. Using a facial recognition algorithm, this system is able to identify signs of sleepiness in a picture. It detects and tracks the driver’s face and eyes to compute the Eye Aspect Ratio (EAR), a validated measure for drowsiness detection. This system reduces fatalities and improves road safety by detecting driver drowsiness using Driving Behaviour Analysis (DBA). The goal of this technology is to make transportation safer by reducing the likelihood of driving errors caused by driver sleepiness.
Eye Detection, and Driving Behavior Analysis.
Spoonie: Self-Stabilizing Feeding Spoon for Tremored Hands
People with Parkinson's disease, the elderly, and infants who have trouble holding their hands steady while eating might benefit from the Spoonie, a self-stabilizing feeding spoon. The spoon's one-of-a-kind design ensures that the head stays put, eliminating the need to worry about shakiness or other undesirable motions that might ruin an otherwise elegant feeding experience. An accelerometer sends real-time data to a microprocessor, which in turn regulates servo motors to keep the spoon head from moving around unexpectedly or shaking the user's hand. Users' quality of life is enhanced since they can self-feed with this spoon. A game-changer in assistive technology, the self- stabilizing spoon allows persons with motor control impairments to tackle real-life problems with ease. In order to make the gadget more tailored to each user's requirements, future improvements might include adjusting the settings for various levels of stability.
Comprehensive Integration and Implementation of Air Quality Monitoring Technology for Respiratory Health in Cardiovascular and Asthmatic Individuals
Air pollution, which harms people's lungs and overall health, is a major problem in the modern world. Charcoal, methane, carbon dioxide, ammonia, and sulfur and nitrogen oxides are some of the dangerous gases emitted by inefficient combustion of agricultural waste and overuse of unconditioned cars. Humans are susceptible to respiratory and cardiovascular illnesses brought on by these contaminants. An IoT-based smart air pollution monitoring system was created to detect air quality and alert people when concentrations surpass safe limits, in accordance with the recommendations of credible sources, in order to tackle this problem. Microcontroller boards like Arduino Uno and ESP32 are used in this system, together with portable gas sensors (MQ2 for smoke and flammable gases like butane and methane, and MQ135 for ammonia, benzene, carbon dioxide, and other dangerous gases). An LCD shows the gas concentration, and data from the sensors is sent to a website using the ESP32 Wi-Fi module. When levels are too high, a bell goes out to let others know. Results from testing the proposed technology to determine gas concentrations in an indoor setting and the associated concentration values are detailed here. It is also possible to expand the system for use in mining and industrial regions with high levels of air pollution. The risk of hospitalization from cardiovascular disease increases by 0.96 percent for individuals over the age of 65 for every hour of exposure to carbon monoxide, according to A.L.N. Sri Datta of the Electronics and Communication Engineering department at Chaitanya Bharthi Institute of Technology in Hyderabad, India (dattaannavarapu9@gmail.com). There was a 6% spike in the hospitalization of non-elderly asthmatic persons when there was 3-7 ppm of carbon monoxide in the air. Individuals with cardiovascular disease or asthma greatly benefit from accurate gas level monitoring systems. Occupational Safety and Health Administration (OSHA) research indicates that at concentrations of 15,000 ppm of CO2, a small number of persons may experience minor respiratory stimulation. Ammonia may aggravate respiratory issues in certain people and has an OSHA-specified maximum concentration threshold of 5 ppm; it also causes allergies similar to asthma. According to Cincinelli et al. in Indoor Air Quality and Health, there is a potential danger to IAQ due to the impact of outside pollution levels and penetration rates on the concentrations to which people are exposed while inside [1].
Enhancing Road Safety with an AI-Powered System to Detect Driver Drowsiness
Drowsiness and fatigue are among the top factors that are considered to contribute to car accidents, causing 1.3 million deaths per year. By using facial landmark identification, the Advanced Drowsiness identification System is able to accomplish its goal of lowering the total number of road accidents caused by various forms of driver weariness and falling asleep behind the wheel. Using a facial recognition algorithm, this system is able to identify signs of sleepiness in a picture. It detects and tracks the driver’s face and eyes to compute the Eye Aspect Ratio (EAR), a validated measure for drowsiness detection. This system reduces fatalities and improves road safety by detecting driver drowsiness using Driving Behaviour Analysis (DBA). The goal of this technology is to make transportation safer by reducing the likelihood of driving errors caused by driver sleepiness. The following terms are associated with this article: EAR, Raspberry Pi, Eye Detection, and Driving Behavior Analysis.
An Investigation of Solar-Powered Grid-Integrated Electric Vehicle Charging Modules for Smart Cities
In response to unpredictable power demands and heightened environmental consciousness about the need to combat climate change and the exhaustion of nonrenewable natural resources, EVs—electric vehicles powered by batteries—are replacing ICE vehicles in the transportation industry. Traditional fossil fuels are neither physically or economically feasible for charging these electric vehicles. A solar-powered electric vehicle (EV) charging module is planned for the future of smart cities in this project. Building a solar-powered electric vehicle charging system that is both efficient and scalable is the major focus of this piece. In order to encourage the use of electric cars and lessen India's reliance on fossil fuels, we want to build a long-term charging infrastructure that makes use of the plentiful sunshine found in metropolitan areas. The model is being implemented in this case using MATLAB/SIMULINK, and the results of the many parameters used, such as the battery voltage, grid voltage, PV array current, and voltage characteristics, are being obtained. Additionally, this project's completion would provide the groundwork for improved, safer, and more user-friendly electric vehicle charging systems in the future. Lastly, many parties involved in smart city infrastructure, including policymakers, EV users, and other organizations, stand to gain from this suggested approach, which aims to provide a sustainable transportation option.
Electric Vehicle Wireless Charging on the Go
The need of efficient, continuous charging solutions is rising in relation to the rising use of electric vehicles (EVs). Traditional plug-in charging facilities sometimes need extended vehicle stops, exacerbating range anxiety among electric vehicle users and leading to unnecessary delays. Wireless road charging, also known as Wireless Power Transfer (WPT), circumvents these limitations by enabling automobiles to charge on the go over wireless equipment already established in the road.By combining Bluetooth-operated robotic cars with wireless charging technology, the proposed technique provides an innovative and creative way to simulate real-life electric vehicle charging scenarios. The system's two primary components are the Vehicle Charging Unit (VCU) and the Vehicle Controlling Unit (VCU). Through the use of inductive coupling, the charging unit converts the electromagnetic field into useable electric power for storage. This energy is transferred from coils buried under the road surface to the receiving coil in the car. Electric vehicle (EV) charging, inductive coupling, wireless power transfer, dynamic charging, and related terms
Transformer Parameters Tracked in Real Time Internet of Things (IoT)
In electrical power systems, a transformer plays a crucial role. Consistent power transmission to consumers is ensured by its dependable functioning. The transformer's real-time health monitoring system extends its life and avoids problems. Using sensors and the Internet of Things (IoT), this article demonstrates how to monitor temperature, load voltage, current, and oil level in real time. Using Arduino UNO, the gathered data were processed. The data is sent to the thingSpeak platform so it may be analyzed. The GSM module notifies a designated individual and sounds an alarm via buzzer when the detected data over the reference limit. The prototype model has confirmed the system's performance. An early warning of the transformer's unusual behavior is provided by the suggested system. This may be useful for reducing unscheduled outages and scheduling preventative maintenance. Transformer real-time monitoring, Arduino, and the Internet of Things (IoT) are all relevant terms.
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.
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