International Journal of Engineering Research and Science & Technology
Editor- In-Chief: Dr. Kenji Suzuki Title proper: International Journal of Engineering Research and Science & Technology Subject: Engineering, Management, Science and Technology Publisher Name: Unicorn Publications ISSN: 2319-5991 Dates of publication: 2013 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Title DOI of the Journal: http://doi.org/10.62643/issn.2319-5991 E- Mail: editor@ijerst.org Impact Factor: 9.625
ENHANCING BLOCKCHAIN-BASED MONEY LAUNDERING DETECTION USING ADVANCED MACHINE LEARNING TECHNIQUES
The rapid adoption of blockchain technology and cryptocurrencies has transformed digital financial ecosystems, offering decentralization, transparency, and security. However, these same characteristics have also enabled sophisticated money laundering activities, where illicit funds are disguised through complex transaction patterns, layering strategies, and anonymization mechanisms. Traditional anti-money laundering (AML) approaches struggle to effectively detect such activities due to the pseudonymous nature of blockchain transactions and the high volume of data generated across distributed networks. This paper presents an advanced machine learning-based framework for enhancing blockchain-based money laundering detection by leveraging both supervised and ensemble learning techniques. The proposed system integrates data preprocessing, feature engineering, transaction graph analysis, and predictive modeling to identify suspicious patterns within blockchain transactions. Key features such as transaction value, frequency, wallet interactions, temporal behavior, and network topology are analyzed to distinguish between legitimate and illicit activities. Advanced models including Random Forest, Gradient Boosting, and deep learning architectures are employed to capture complex nonlinear relationships and improve detection accuracy. Additionally, the framework incorporates anomaly detection and risk scoring mechanisms to enhance interpretability and support real-time monitoring. Experimental evaluations demonstrate that the proposed approach achieves high accuracy, precision, recall, and F1-score, outperforming traditional detection methods while significantly reducing false positives and false negatives. The system also exhibits strong scalability and adaptability to evolving laundering techniques, making it suitable for large-scale blockchain environments. By combining advanced machine learning algorithms with blockchain analytics, this research contributes to the development of intelligent, automated, and reliable solutions for combating financial crimes in decentralized ecosystems, thereby strengthening trust, compliance, and security in digital financial systems.
Blockchain, Money Laundering Detection, Machine Learning, Cryptocurrency, Fraud Detection, Anomaly Detection, Ensemble Learning, Financial Security.
1 Ms. M Gangalatha, 2 Shaik Mohammed Ali, 3 Mrs. Pemma Radhika
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
ADVANCED DAMAGE DETECTION OF ROADSIDE INFRASTRUCTURE USING ATTENTION-BASED DEEP LEARNING MODELS
The maintenance and monitoring of roadside infrastructure, including guardrails, reflective panels, traffic signs, and other ancillary facilities, are critical for ensuring road safety and efficient transportation systems. Traditional inspection methods rely heavily on manual surveys, which are time-consuming, labor-intensive, and prone to human error, especially in large-scale and complex environments. With the advancement of computer vision and deep learning, automated damage detection has become a promising solution; however, existing models often struggle with challenges such as small target detection, varying illumination conditions, occlusions, and complex backgrounds. This paper proposes an advanced attention-based deep learning framework for accurate and real-time damage detection of roadside infrastructure. The proposed approach integrates convolutional neural networks with adaptive attention mechanisms to enhance feature extraction and improve the detection of fine-grained damage patterns. Specifically, multi-scale feature fusion is employed to capture both global context and local details, while attention modules dynamically focus on relevant regions, reducing the impact of background noise. Additionally, the model incorporates optimized loss functions and lightweight architectural components to achieve high detection accuracy with reduced computational complexity, making it suitable for deployment in edge computing environments such as drones and roadside monitoring systems. Extensive experiments conducted on real-world datasets demonstrate that the proposed model outperforms conventional deep learning approaches in terms of accuracy, precision, recall, and processing speed. The results highlight significant improvements in detecting small and subtle damages under challenging conditions. The proposed system not only enhances detection performance but also supports scalable and automated infrastructure monitoring, contributing to improved maintenance efficiency, reduced operational costs, and enhanced road safety.
Roadside Infrastructure, Damage Detection, Deep Learning, Attention Mechanism, Computer Vision, YOLO, Smart Transportation, Edge Computing
1 Mr. H Madhusudhana Rao, 2 Manikanta Goparapu, 3 Mrs. Pemma Radhika
IMPLIMENTAION OF AN FSM - CONTROLLD AHB TO APB BRIDGE
Advanced Microcontroller Bus Architecture (AMBA) protocols are widely used in System-on-Chip (SoC) design to enable efficient and structured communication between high-speed and low-speed peripherals. This paper presents the design and implementation of a Finite State Machine (FSM)-controlled bridge between the Advanced High-performance Bus (AHB) and the Advanced Peripheral Bus (APB). The AHB bus is designed for high-speed communication with features such as pipelined operations, burst transfers, and high bandwidth, whereas the APB is optimized for low-power, low-complexity communication with peripheral devices. The proposed bridge acts as an interface that converts AHB transactions into APB-compatible signals, ensuring seamless communication between the two buses despite their differences in speed and protocol structure. The design incorporates a well-defined FSM to control the sequence of operations, including address decoding, data transfer, and control signal generation. This structured approach ensures proper synchronization, reduces data transfer latency, and maintains signal integrity throughout the communication process. The entire system is implemented using Hardware Description Language (HDL), ensuring modularity and scalability, and is verified through simulation to validate its functional correctness. Simulation results confirm that the bridge successfully handles read and write operations, maintains protocol compliance, and achieves reliable data transfer between AHB and APB interfaces. Overall, the proposed FSM-controlled AHB-to-APB bridge provides an efficient, low-complexity, and scalable solution for integrating different bus architectures within modern SoC designs, making it highly suitable for embedded systems and real-time applications.
AI BASED IMAGE CLASSIFICATION NUTRITION ESTIMATOR FOR WEIGHT ANALYSIS
In recent years, artificial intelligence has significantly transformed healthcare and nutrition analysis by enabling automated and accurate dietary assessments. This paper proposes an AI-Based Image Classification Nutrition Estimator for Weight Analysis that uses deep learning techniques to identify food items from images and estimate their nutritional content, including calories, proteins, carbohydrates, and fats. The system integrates convolutional neural networks (CNNs) for image classification and a nutritional database for mapping food items to their nutritional values. The primary objective is to help users monitor their diet and manage body weight effectively without manual tracking. Traditional nutrition tracking methods are timeconsuming and error-prone, whereas the proposed system provides real-time analysis through simple image input. The model is trained on a diverse dataset of food images to improve accuracy across different cuisines and food presentations. This system can be used in fitness applications, hospitals, diet planning, and personal health monitoring. By combining computer vision and machine learning, the proposed solution enhances accessibility, efficiency, and precision in dietary assessment and weight management.
DYAWARSHETTY SRUJANA, GUDEPU VINAY, GUGULOTHU KRANTHI, KARNATI SHIVA, JANGA AJAY, VASANNAGARI PAVANI
IDENTIFY REAL VS AI GENERATED IMAGES USING CNN AND GRAD-CAM
In the recent developments, synthetic data has been developed and it has become easy to distinguish between the real and the created photograph. It is harder to navigate the ones created by a machine. The ease at which images created by AI can be found with the help of computer vision and other forms of informatics is significant as people must be able to trust and verify the information. Fake data have been created and compared with real pictures with the assistance of latent diffusion. It applies computer vision algorithms, especially CNN2D, in sorting the images into groups. A more advanced CNN2D was developed. It is pretty good with the level of precision of 95.77. This will enable more photographs created by individuals to be discovered more often, as well as the growing problem of AI-generated content. These results indicate that computer vision is not a mere desirable attribute; rather it is a powerful tool that can identify genuine and counterfeit pictures that can be used to ensure that digital data is correct and valid.
Mr.B.Kishore kumar , A.Gayathri, G. Vyshnavi, D. Shivakumar
A PREDICTIVE DATA FEATURE EXPLORATION-BASED AIR QUALITY PREDICTION APPROACH
Rapid population growth combined with accelerated urbanization and economic expansion has significantly intensified environmental degradation in major cities across India. Among various environmental challenges, air pollution has emerged as a critical concern due to its severe implications for public health, ecological balance, and sustainable urban development. Predicting air quality levels is a complex task because pollutant concentrations exhibit strong temporal variability, nonlinearity, and dependence on multiple external factors. This study proposes an advanced air pollution prediction framework using an improved Light Gradient Boosting Machine (LightGBM) model to forecast air quality levels in selected Indian cities. The proposed system integrates historical pollution data with forecasting-based auxiliary inputs to enhance predictive capability. A sliding window strategy is employed to effectively capture high-dimensional temporal dependencies and manage large-scale time-series data. Experimental evaluation demonstrates that the enhanced LightGBM model outperforms conventional machine learning approaches in terms of prediction accuracy, stability, and robustness. The findings confirm the suitability of the proposed approach for real-time air quality forecasting and decision support in smart city and environmental monitoring applications
Job Demand Forecasting, Patent Classification Codes, Text Embeddings, Occupational Data Analysis, Workforce Analytics, Technology–Skill Mapping, Labor Market Intelligence, Semantic Similarity, Innovation Impact Assessment, Employment Trend Prediction
BLOOD ONCOLOGY -REVOLUTIONIZING BLOOD CANCER DETECTION AND CLASSIFICATION WITH DEEP LEARNING
Blood cancer affects the production and functioning of blood cells, making diagnosis difficult due to the presence of multiple subtypes and overlapping symptoms. This research proposes a novel blood cancer detection system based on a fine-tuned VGG16 Convolutional Neural Network (CNN) optimized using the Adam optimizer. The framework begins with an image preprocessing stage that includes standardization to a resolution of 224 × 224 pixels and data augmentation techniques such as rotation, flipping, and zooming to improve model robustness against data variability. The VGG16 model effectively extracts both low-level and high-level features such as boundaries, textures, shapes, and complex patterns from microscopic blood cell images, enabling accurate classification. The use of the Adam optimizer allows continuous learning rate optimization, ensuring faster convergence, improved generalization, and better handling of sparse or noisy datasets. The optimized VGG16 model is deployed through a Flask-based web application for real-time medical image analysis, achieving an accuracy of 96%. This system offers a precise, efficient, and user-friendly solution to assist healthcare professionals in the early detection and classification of blood cancer, thereby supporting improved diagnosis and treatment outcomes.
Deep Learning, Blood Smear, Cancer Detection.
SATELLITE IMAGE CLASSIFICATION OF WATER, ICE, AND LAND USING DEEP LEARNING
Satellite image classification plays a crucial role in environmental monitoring, climate analysis, and resource management. The ability to accurately distinguish between water, ice, and land surfaces is essential for applications such as glacier tracking, flood monitoring, and land-use planning. Traditional image processing techniques often struggle with spectral similarities and noise in satellite imagery. To address these challenges, deep learning methods, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for automated image classification. This paper presents a deep learning-based approach for classifying satellite images into three primary categories: water, ice, and land. The proposed model leverages high-resolution satellite datasets and applies preprocessing techniques such as normalization, resizing, and augmentation to enhance model performance. A CNN architecture is designed to extract spatial features and learn complex patterns that differentiate the three classes effectively.
K.SUDHA RANI, K.SRI SAI BHAVYA, K.PRATHYUSHA, K.YASHWANTH, K.NARENDAR
REGENERATIVE BRAKING SYSTEM USING DYNAMO IN ELECTRIC UTILITY TRUCK
This project presents the implementation of a regenerative braking system in an electric utility truck using a dynamobased mechanism. The system aims to improve energy efficiency by recovering kinetic energy lost during braking. Instead of dissipating energy as heat, the dynamo converts mechanical energy into electrical energy. This generated energy is stored in the battery for future use. The system works alongside conventional braking to ensure safety and reliability. The design integrates both electrical and mechanical components effectively. It enhances vehicle performance by extending battery life and driving range. The system also reduces overall energy consumption. This approach promotes eco-friendly transportation solutions. Hence, it contributes to sustainable energy utilization in electric vehicles.
Regenerative Braking System, Dynamo, Electric Vehicle, Energy Recovery, Kinetic Energy, Battery Charging, Energy Efficiency, Sustainable Transportation, DC-DC Converter, Hybrid Braking System
1 S. J. V. Rama Sai, 2K. Gopi, 3G.N. S. T. Bhaskar, 4Mr. G.Rajesh
IOT BASED PATIENT MONITORING SYSTEM WITH RFID BASED HEALTH CARD INTEGRATION AND SMART VENTILATOR SUPPORT
The IoT-Based ICU Patient Monitoring System Integrated with RFID-Based Health Card Technology is an advanced healthcare solution designed to enhance patient safety, real-time monitoring, and efficient medical data management in Intensive Care Units (ICUs). In critical care environments, continuous monitoring of vital parameters such as heart rate, body temperature, oxygen saturation (SpO₂), blood pressure, and ECG signals is essential for timely diagnosis and intervention. Traditional monitoring systems often require manual observation and lack seamless integration with patient medical records, leading to delays and potential errors. This proposed system leverages Internet of Things (IoT) technology to collect real-time physiological data from sensors attached to the patient’s body. These sensors continuously transmit data to a central monitoring unit or cloud platform, enabling doctors and healthcare staff to access patient information remotely through web or mobile applications. The integration of RFID-based health cards further enhances the system by providing instant access to a patient’s medical history, identification details, and treatment records. Each patient is assigned a unique RFID card, which, when scanned, retrieves their complete health profile from the database.
K.UMA, G.NARSA REDDY,J. ARAVIND, G. SAHITYA, Mrs.J.GOUTAMI
Comparative Design and Performance Analysis of Sliding Mode Control Techniques for BLDC Motor Speed Regulation in EV Applications
Brushless DC (BLDC) motors offer significant advantages over conventional DC motors, including high efficiency, low noise, enhanced reliability, longer lifespan, and a high torque-to-weight ratio, making them well suited for electric vehicle (EV) applications. However, precise speed regulation is essential to ensure optimal performance under varying load conditions. This paper presents a comparative analysis of three Sliding Mode Control (SMC) techniques—first-order SMC, second-order SMC, and Integral SMC—for BLDC motor speed control. The study considers a BLDC motor rated at 1 kW, 3000 rpm, operating under a load torque of 3 N·m. The performance of each control strategy is evaluated in terms of steady-state error, stability, and dynamic response. Simulation results obtained using MATLAB/Simulink demonstrate that the Integral SMC approach outperforms the other methods, achieving the lowest steady-state error of 2.4 rpm, compared to 4.9 rpm for firstorder SMC and 9.5 rpm for second-order SMC. The findings indicate that Integral SMC provides superior accuracy and robustness for speed regulation. Overall, the proposed analysis confirms the suitability of BLDC motors and advanced SMC techniques for efficient and reliable operation in electric mobility applications such as e-bikes and tricycles.
Brushless DC Motor (BLDC), Speed Control, Electric Vehicles (EV), Sliding Mode Control (SMC), First-Order SMC, Second-Order SMC, Control Systems.
Mr K .Naveen Babu1 , Pathan Salma2 , Namburi Arya Prabha3 , Dirisala Jyothi Sri4 , Mudugu Naga Lakshmi5
COVERSION OF ELECTRICAL VEHICAL INTO UTILITY EV TRUCK
The project focuses on the conversion of an existing electric vehicle into a utility electric truck for practical applications. The aim is to enhance vehicle performance and load-carrying capacity through mechanical and electrical modifications. The existing motor is upgraded to provide higher torque and efficiency for better performance. A new chassis is designed to support additional payload and improve structural strength. The suspension system is modified to ensure stability under heavy loads. Battery capacity is increased to extend the driving range and improve energy storage. The project integrates various engineering concepts including mechanical design and electrical systems. The redesigned vehicle ensures better efficiency and reduced energy consumption. Regenerative braking is considered to improve energy recovery. The system is eco-friendly as it produces zero emissions. It reduces dependence on fossil fuels and supports sustainable transportation. The conversion process is cost-effective compared to purchasing a new EV truck. The developed prototype is suitable for short-distance transport applications. The project demonstrates the feasibility of EV conversion for utility purposes. Overall, the system provides a practical and economical solution for green mobility.
Electric Vehicle, Utility Truck, BLDC Motor, Battery System, Chassis Design, Regenerative Braking, Sustainable Transportation, Energy Efficiency, EV Conversion, Green Mobility
1Gollangi Sai Kumar, 2 S Mani Sai Prasad, 3N Sai Prasanth, 4N Abhishek, 5DR. I. Harish
AUTOMATED FASHION IMAGE TAGGING SYSTEM USING DEEP LEARNING
The rapid growth of e-commerce and digital fashion platforms has led to an overwhelming volume of fashion images, creating a strong need for efficient organization, retrieval, and recommendation systems. This project, “Automated Fashion Image Tagging System Using Deep Learning,” aims to develop an intelligent solution that automatically generates descriptive tags for fashion images using advanced deep learning techniques. The system leverages Convolutional Neural Networks (CNNs) and transfer learning models such as ResNet and VGGNet to extract visual features like color, texture, pattern, and clothing type from input images. The proposed system processes fashion images through a trained deep learning model that identifies multiple attributes, including garment category (e.g., shirt, dress), style (casual, formal), color, and patterns (striped, floral). A multi-label classification approach is adopted to assign multiple relevant tags to a single image, improving the accuracy and usefulness of metadata. The dataset used for training consists of labeled fashion images, preprocessed through resizing, normalization, and augmentation techniques to enhance model performance and generalization. The system architecture integrates image preprocessing, feature extraction, model training, and tag generation modules, enabling real-time or batch-based tagging. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring reliable and consistent results. The automated tagging system significantly reduces manual effort, improves searchability, and enhances recommendation systems in online retail platforms. Overall, this deep learning-based approach provides a scalable, efficient, and intelligent solution for fashion image management, contributing to advancements in computer vision applications within the fashion industry.
CH. TULASI RATNA MANI, OGURIELIJA, NARALA HEMANTHVEERAKRISHNA, PABBOJU VIKRANTH, MAILARAM AKHIL
Smart Crop Care: AI-Based Crop Prediction, Fertilizer Recommendation and Plant Disease Detection using Machine Learning
Plant diseases cause 20–40% of global crop losses annually, leading to over 220 million metric tons of food waste and economic losses exceeding $147 billion. In India, where agriculture supports over 150 million farmers and contributes 18% to GDP, fungal and bacterial diseases severely affect major crops such as wheat, rice, and vegetables. Limited access to plant pathologists in rural areas often results in misdiagnosis and delayed treatment, causing field losses of 25–50%. Existing plant disease detection systems primarily use CNNbased models, such as VGG architectures, to classify diseases from leaf images but lack actionable guidance and support services. To overcome these gaps, this research proposes the Integrated Agro-Diagnostic and Remediation Framework (IADRF), an enhanced CNN-based system trained on the Plant Village dataset with over 61,000 images. IADRF not only detects plant diseases but also provides prevention measures, treatment recommendations, and integration with agricultural e-commerce platforms, enabling timely intervention and promoting sustainable farming practices
ADAPTIVE LEARNING AND ENGAGEMENT MONITORING SYSTEM FOR SKILL TRAINING ENVIRONMENTS
The project Adaptive Learning and Engagement Monitoring System for Skill Training Environments presents an intelligent learning solution designed to improve personalized education. It integrates data from platforms like Moodle, BookRoll, and Learning Record Store (LRS) to analyze student learning behavior, quiz performance, and engagement patterns. The system uses Sentence-BERT (SBERT), a deep learning model, to generate semantic embeddings and accurately match incorrectly answered quiz questions with relevant learning materials. This approach overcomes limitations of traditional methods and ensures contextaware recommendations. By providing precise and personalized content suggestions, the system helps learners focus on weak areas, reduces cognitive load, and enhances overall learning outcomes, making the learning process more efficient and effective.
1Mrs. P. PRASHAMSA, 2 S. PREMALATHA, 3 T. ANKITHA, 4 V. MANIKANTA
DETECTING GLAUCOMA STAGES USING DEEP LEARNING
Glaucoma is a major cause of irreversible blindness worldwide, often progressing without noticeable symptoms until significant damage has occurred to the optic nerve. Early detection is crucial to prevent vision loss, yet manual screening of retinal fundus images requires expert ophthalmologists and is not always accessible. This project presents a Glaucoma Identification System, an automated deep learning-based solution designed to detect and classify glaucoma severity from retinal fundus images. The system employs an ensemble of UNet++ segmentation models with multiple encoder backbones, including ResNet34, InceptionV4, and DenseNet121, enhanced with SCSE attention mechanisms. These models collaboratively segment the optic disc and optic cup regions, enabling precise extraction of structural features. The segmented outputs are used to compute the Cup-to-Disc Ratio (CDR), a clinically significant indicator for glaucoma assessment. Based on CDR values, the system classifies cases into four stages: Non-Glaucoma, Mild, Moderate, and Severe. The model is trained on publicly available datasets such as REFUGE and ORIGA, using advanced preprocessing techniques and combined loss functions to improve segmentation accuracy.
1Mrs. V PRAVALIKA, 2MARRI HARIKA, 3P RAKESH KHANNA, 4K RAVINDRA NATH TAGORE, 5K VENKATMUKUND SAI
ELECTRIC VEHICLE-TO-VEHICLE (V2V) ENERGY TRANSFER USING MOTOR INVERTER AND WINDING
The increasing adoption of electric vehicles (EVs) has created a growing demand for flexible and decentralized energy management solutions. This paper presents an Electric Vehicle-to-Vehicle (V2V) energy transfer system that leverages the existing traction motor inverter and stator windings of EVs to enable direct peer-to-peer power exchange without additional DC-DC converters or external charging hardware. The proposed topology connects two EVs through their standard charging interfaces, with the source EV battery supplying DC power to its traction inverter, which converts it into controlled AC power using three-phase interleaved Pulse Width Modulation (PWM). The motor stator windings act as inductive elements for current regulation and energy coupling. On the receiving side, the transferred AC power is rectified into DC to charge the battery. A sophisticated control strategy ensures zero torque operation, preventing motor rotation during charging. The system operates across a power range of 10–150 kW with a simulation switching frequency of 220 Hz–3.1 kHz, achieving superior efficiency through single-stage power conversion, Zero Voltage Switching (ZVS), and optimized low-frequency operation. MATLAB/Simulink simulation results validate stable power flow, reduced current ripple, and effective PWM-based control. Compared with state-of-the-art V2V methods, the proposed topology demonstrates higher power efficiency, reduced hardware cost, lower switching losses, and elimination of additional mechanical braking requirements.
Vehicle-to-Vehicle (V2V), electric vehicle, traction inverter, motor windings, bidirectional power transfer, interleaved control, zero torque operation, Zero Voltage Switching (ZVS), pulse width modulation, peer-to-peer energy transfer
Mr. N. V. Koteswara Rao¹, Godavarthi Deekshitha², Alla Chandana Reddy³, Nakkala Naga Lakshmi⁴, Gutti Divya⁵
DESIGN AND IMPLEMENTATION OF 3D PRINTER USING ARDUINO MEGA 2560
The development of a cost-effective, efficient, and user-friendly additive manufacturing system. The study focuses on designing and fabricating a 3D printer based on Fused Deposition Modeling (FDM) technology, where thermoplastic material is deposited layer by layer to create three-dimensional objects from digital models. The system utilizes an Arduino Mega 2560 microcontroller as the central control unit, enabling precise coordination of motion, temperature regulation, and material extrusion. The printer integrates essential components such as stepper motors for accurate axis movement, motor drivers for motion control, an extruder assembly for filament melting and deposition, and a heated bed to ensure proper adhesion and minimize warping. The working process involves converting a 3D model into G-code using slicing software, which guides the printer in producing the object layer by layer with high precision and repeatability. This project emphasizes affordability and accessibility, making it suitable for educational purposes, hobbyists, and small-scale manufacturing applications. It demonstrates the effective integration of hardware and software systems to transform digital designs into physical prototypes. The developed system has broad applications in rapid prototyping, product development, and engineering education. Furthermore, the project highlights the future scope of enhancements such as increased printing speed, multi-material capability, and advanced automation, reinforcing the growing significance of 3D printing in modern manufacturing
1MAMIDI SAI KIRAN, 2KADALI CHANDRA SHEKAR, 3BORRA PURNA ISWARYA RAVI TEJA KUMAR, 4Mr. M.R.S.NATARAJ, 5Dr. IPPILE HARISH
EXPERIMENTAL ANALYSIS OF EVAPORATIVE CONDENSER BASED VAPOUR COMPRESSION REFRIGERATION SYSTEM
Refrigerators work on vapour compression refrigeration cycle. In the refrigerator, evaporator, condenser, compressor and capillary tube are important parts. The performance of refrigerator depends on the performance of these four main parts. The evaporator and condenser work as a heat exchanger. Generally, air is used to cool the refrigerant vapour in condenser. But due to low heat carrying capacity of air, mass of air is high and large condenser is required. The size of condenser and performance of refrigerator is improved by using water as a cooling medium. The objective of this study is to investigate the performance of refrigerator by using Evaporative condenser in which water is used as cooling fluid. A refrigerator of 220 liter capacity was used to develop the experimental setup. The experiment was carried out on developed setup with air cooled condenser and evaporative condenser. Then results of both experiments were compared. The result shows that the coefficient of performance (COP) of refrigerator is improved by 4.1% using evaporative condenser.
Vapour compression refrigeration system, Air cooled condenser, Evaporative condenser, Coefficient of performance, Refrigerator
Dr. Jagdish Shanabhai Talpada1 , Chetan A Somani2
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
Structural Behaviour of Commercial Building with and without Hanging Column Structures
Because more people are moving to cities, tall structures generally don't have enough room. Wind gusts may impact the structure from any side. These gusts might affect the building from any side. These gusts have had an influence on the structure in both directions during the previous several years. The goal of these designs is to make the projects they work on seem better. Because the floor height changes, the structure's rigidity is not the same at the level of the soft story. Changes in the floor's height lead to this happening. Even if winds reveal this break, it might still cause structures to The goal of this work was to do a static analysis of three-dimensional building frames that included G+7 stories, hanging columns, and soft-story parts. parts. The remaining sixty-four instances have hanging columns on one level, and the soft story goes straight from the ground (G) story to the G+7 floor. Eight of the cases have a center. Hanging columns on any one of the floors, whereas sixty-four of the other instances feature Hanging columns at a specified level. This case looks at a total of seventy-three cases. Moreover, we create a simple example in which neither the stories nor any of the columns float, in accordance with the previously mentioned constraints. Along with the constraints already mentioned, we make a basic example where neither the floors nor any of the columns float. To get the look we wanted, we changed the heights of the floors. We used the maximum node displacements (resultant), maximum moments, maximum shear force, maximum axial force, and maximum storey drift to do the study. To get technical conclusions, it is required to analyze the results.
P. Jagadeeswara Reddy 1, B. Sri Datta Subramanyam 2, Kadiyala Lahari Venkata Durga 3, Morukurthi Mounika 4, Bandaru Satish 5, Saitana Satyanarayanaswamy 6
Structural Analysis and Design of Commercial building by using Etabs
Structural Analysis is a branch which involves in the determination of behavior of structures in or- der to predict the responses of different structural components due to Effect of loads. Each and every structure will be subjected to Either one or the groups of loads, the various kinds of loads Normally considered are dead load, live load, earth quake load and wind load. ETABS (Extended Three-Dimensional Analysis of Building System) is a software which is incorporated with all the major analysis engines that is static, dynamic, Linear and non-linear, etc. and especially this Software is used to analyze and design the buildings. Our project “Analysis and Design of Commercial building using ETABS software”. A Multi-storied building is considered for this study. Analysis is carried out by static method and design is done as Per IS 456:2000 guidelines. Also, an attempt has been made to Design the structural elements manually. Drawing are done using Auto CAD.3D Modelling is done using Revit Architecture.
Dr. B. Satyanarayana 1, Kada Devi Udaya Bhanu 2, Sunkara Ishwarya Ambika 3, Bikkina Bhavani 4, Motupalli Venkata Subba Rayudu 5
Real-Time Detection of Unauthorized Drones in Restricted Areas
This paper presents an intelligent, vision-based drone detection and alert system designed to automatically identify and track unauthorized drones in restricted airspace. The proposed solution integrates computer vision, deep learning, and IoT-based communication for real-time monitoring and alert generation. The system employs a convolutional neural network (CNN) and YOLO-based object detection algorithm to distinguish drones from birds, planes, and other airborne objects with high accuracy. The model processes live video streams captured from surveillance cameras and triggers instant alerts via SMS, email, or mobile notifications when a drone is detected. The backend is implemented using Flask for model inference and REST API communication, while the frontend dashboard enables real- time visualization of detection events. The system architecture also supports the addition of multiple cameras and continuous background operation to ensure uninterrupted surveillance. Experimental evaluations demonstrate high detection precision, fast response time, and robust performance under diverse lighting and environmental conditions. This solution provides a scalable, low- cost framework for enhancing airspace security and situational awareness in sensitive environments such as airports, defense zones, and public gatherings.
K Kiran Prakash 1, Banothu Govardhan 2, Kotakonda Adla Nithin 3
Performance in the Seismic Analysis of a High Rise Multi-Storied with a Resilient concrete structure by Etabs
A natural disaster known as an earthquake has been responsible for the loss of millions of lives over the course of human history. One of the most damaging occurrences that has ever been recorded in India, both in terms of the number of deaths and the amount of damage to infrastructure, was the earthquake. As a result of the current state of the environment and the behavior of tectonic plates, it is of the highest importance for civil engineers to take into consideration the consequences of earthquakes for the purpose of constructing buildings. In addition, the majority of India is located in an earthquake-prone zone; hence, it is essential to take seismic load into consideration while building a structure in order to reduce the impact of an earthquake. Both the equivalent lateral force technique and the response spectrum approach are used in this project work to conduct an analysis of a high-rise structure that is fourteen stories tall and located in seismic zone V. Following completion of the study, a number of response characteristics, including story shear, story displacement, story drift, and others, are being investigated, and the outcomes are being compared. In addition, the study of the building that is now under construction is carried out, and the findings of foundation shear tested at various story levels throughout the construction period are compared.
D.Rohin Kumar 1, Dr. K.Saroja Rani 2, Gonela Hema Latha Kalyani 3, Guda Shalem Raj 4, Ramarlapudi Lakshmi Narayana 5, Karanam Venkata Durga Raghavendra6
Evaluating the Effects of Seismic Behaviour on the Structural Behaviour of Irregular buildings
People have been building taller buildings for a long time because expansion in horizontal space has made them want to reach higher. However, further extending the vertical limit increases the risk factor significantly. For safety, we need to check the building for various loads. We must examine a building to determine its earthquake resistance. This is because the way a building behaves during an earthquake is crucial. It is difficult to determine when an earthquake will bring down a tall building. So, we need to undertake a seismic force assessment on different kinds of buildings. It is possible for the seismic office to utilize the seismic coefficient method to look at buildings that are modest to medium-sized and up to forty meters tall. This type of analysis requires less manual work to complete. The general shape, size, and geometry of a structure all play a big part in how it acts. This is because asymmetrical constructions are more likely to behave badly during an earthquake than symmetrical ones. The traits that define symmetrical, U-shaped, and T-shaped structures are important to consider. We create RC structures during earthquakes to learn more about how different forms might change the way seismic loads and behavior work. The ETABS software helps with the seismic coefficient research of a 10-story building with levels that are three meters high and a plan form that varies, including symmetrical, U-shaped, and T-shaped configurations. It takes a long time to evaluate a 15-story building, which makes mistakes more likely. ETABS can make analyzing a structure easier, faster, and more accurate. We follow IS 1893:2002 (Part 1) when we do the analysis. RC structures in the shape of a "y" have their reaction, which includes lateral pressures, base shear, storey drift, and storey shear, among other things. We compare the results by looking at how the structures of different shapes react. Keyword: Earthquakes, L-shaped, and T-shaped, ETABS software, IS 1893:2002 (Part 1), Lateral pressures, Base shear, Storey drift, and Storey shear
B.Sri Datta Subhramanyam 1, Dokkadi Madhulatha2, Abbireddy Toni Vibhath Siva Manideep 3, Thurangi Phaneendra kumar 4, Samatam Harsha Sainadh 5
Experimental Investigation on Geotechnical Characterization of Organic clay soil and its stabilization using Cement and Nano silica Additives
Organic soils containing low to moderate organic matter are commonly found in several regions of Kerala. Such soils are highly compressible, possess low shear strength, and exhibit poor load-bearing capacity, making them unsuitable for supporting structural loads without improvement. The presence of organic matter increases waterholding capacity, resulting in high plasticity and reduced cohesion. With rapid urbanisation, industrial growth, and increasing population, the demand for civil infrastructure has risen significantly, necessitating the use of marginal soils. While soils with very high organic content are generally avoided for construction, those with low to moderate organic matter can be improved through proper characterisation and stabilisation techniques. The present study focuses on evaluating the geotechnical behaviour of organic clay soils with low to moderate organic content and enhancing their engineering properties using chemical stabilisation methods. Initially, a comprehensive experimental programme was carried out to assess the influence of organic content and type of organic matter on soil properties, including consistency limits, compaction characteristics, consolidation behaviour, and shear strength. Inorganic clay was collected from Pantheerankavu near Calicut, along with commercially available sodium bentonite and kaolinite, representing different clay mineralogical compositions. Organic clay soils were artificially prepared by adding starch-based organic matter in proportions ranging from 2% to 15%. Naturally occurring organic soils containing decomposed wood fibres were also examined for comparison, with organic contents between 5% and 12%. Additionally, the effects of different organic matter types—starch, urea, and compost—were investigated. Results indicated that increasing organic content significantly alters soil behaviour, shifting plasticity from medium to high. Maximum dry density decreased while optimum moisture content increased due to enhanced water retention and soil particle disintegration. Compressibility increased markedly, leading to higher consolidation settlements, while unconfined compressive strength and shear strength decreased substantially with organic content. Strength stabilisation was observed after 28 days of curing. The influence of organic matter was more pronounced in bentonite and kaolinite-rich soils compared to illitic clays. Among organic additives, starch-based soils showed relatively lower compressibility and higher strength. To improve soil performance, eco-friendly stabilisation using cement, nano-silica, and their combination (1% dosage) was adopted. The combined cement–nano-silica treatment provided superior strength enhancement due to accelerated pozzolanic reactions.
B Sri Kalyan 1, Dera Mahesh 2, Parasa Mahanvitha 3, Nekkanti Yajath Venkat Ram 4, Sangisetti V V Subrahmanyam 5
MULTIMODAL MISINFORMATION AND DEEPFAKE DETECTION SYSTEM
The exponential growth of social media platforms has significantly accelerated the spread of misinformation across diverse content formats, including textual narratives, images, memes, and videos. Conventional fake news detection approaches remain insufficient as they are predominantly designed for textual data, failing to address the complexities introduced by multimodal content and sophisticated deepfake media. This work proposes a comprehensive artificial intelligence-driven system for detecting misinformation and identifying deepfake content within a unified architecture. The system incorporates Natural Language Processing (NLP), Computer Vision (CV), Convolutional Neural Networks (CNN), and Retrieval-Augmented Generation (RAG) to deliver accurate, interpretable, and context-aware outcomes. Textual inputs are processed using advanced NLP techniques to extract key claims, while Optical Character Recognition (OCR) retrieves embedded textual information from images and memes for multimodal fusion. Video inputs employ frame-based CNN analysis, with frame-level predictions aggregated into a final classification. The system produces conclusive output categorizing content as True, False, or Misleading, accompanied by a confidence score and evidence-backed explanation, enhancing user trust and suitability for real-world applications
Deepfake Detection, Multimodal Learning, Misinformation, Retrieval-Augmented Generation, Convolutional Neural Networks, Fact-Checking
Ruthala Sridivya1 , Ryali Sree Sai Bhavitha Sumali1 , Sai Gayatri Yellajosyula1 , Sathivada Gayathri1 , Sattaru Pallavi1
SMART STREET – GARBAGE DETECTION & ALERT SYSTEM
The rapid growth of urban populations has significantly increased waste generation, making efficient garbage management a critical challenge for smart cities. This project proposes a Smart Street Garbage Detection and Alert System that leverages computer vision and machine learning techniques to automatically detect garbage accumulation in public areas. The system utilizes image processing models, such as convolutional neural networks (CNNs), to identify waste in real-time through surveillance cameras or mobile inputs. Upon detection of garbage, the system generates automated alerts to municipal authorities, enabling timely waste collection and improved sanitation. Additionally, the system maintains a centralized database for monitoring garbage levels, tracking locations, and analyzing patterns for better decisionmaking. This approach reduces manual inspection, enhances operational efficiency, and promotes cleaner urban environments. The proposed solution contributes to the development of smart cities by integrating artificial intelligence with waste management systems, ensuring sustainability, public hygiene, and environmental protection.
MS. V.KONICA NEHAL, SETTI LAHARI, TELUGU KALYANI, SHASHANK TIWARI, SINGAREDDY SUNNY SHARAN REDDY
REAL-TIME COTTON LEAF DISEASE DETECTION AND PESTICIDE RECOMMENDATION USING DEEP LEARNING AND CONVERSATIONAL AI
The increasing susceptibility of cotton crops to various diseases significantly affects agricultural productivity, particularly in regions where cotton is a major commercial crop. This project presents a real-time cotton leaf disease detection and pesticide recommendation system that integrates deep learning and conversational AI to support farmers in early diagnosis and effective decisionmaking. The proposed system utilizes a two-stage approach, where a YOLO-based model detects infected regions in cotton leaf images, followed by an EfficientNetbased classifier that identifies specific disease categories with high accuracy. In addition to disease classification, the system incorporates confidence-based decision logic to handle uncertain predictions and enhance reliability in realworld conditions. A curated knowledge base provides appropriate pesticide recommendations, including dosage, application guidelines, and safety measures. Furthermore, a multilingual chatbot interface enables farmers to interact with the system and receive context-aware advisory support in an accessible manner
1Mrs. P.LAXMI PRASANNA, 2PANCHALA MANOJ KUMAR, 3KURREMULA SRUJANA, 4NADIMPALLY ABHIROOP, 5POLAGANI SOMESH
Image to Text Conversion with Voice Announcement using RaspberryPi
This project presents the design and implementation of an image-to-text conversion system with voice announcement using a Raspberry Pi, aimed at providing an efficient and low-cost assistive solution. The system captures images through a camera module and applies image preprocessing techniques to enhance quality for accurate recognition. Optical Character Recognition (OCR) is utilized to extract textual information from the captured images, which is then converted into speech using a text-to-speech (TTS) engine. The generated audio output is delivered through a speaker, enabling users to hear the extracted content in real time. This system is particularly beneficial for applications involving Smart reading system, Document digitization, Translation systems. The integration of image processing, OCR, and speech synthesis technologies ensures a compact, portable, and user-friendly solution. However, the system’s accuracy is influenced by factors such as lighting conditions, image clarity, and font variations. Future enhancements may include support for multiple languages, improved recognition accuracy using advanced algorithms, and real-time video processing capabilities, making the system more robust and versatile
DHEEKONDA SWATHI1 , VOLLALA VAISHNAVI2 , SABAVATH CHAMANTHI3 , RATHLAVATH KALAVATHI4 , SALPALA CHANDU5
EMOJIS TO EMOTIONS: A HOLISTIC MULTIMODAL APPROACH FOR MENTAL HEALTH MONITORING ON SOCIAL MEDIA PLATFORMS
The widespread use of social media has transformed how individuals express emotions, with emojis emerging as a powerful non-verbal communication tool alongside text. However, most existing mental health monitoring systems rely primarily on textual analysis, overlooking the emotional depth conveyed through emojis and user behavior patterns. This project proposes a holistic multimodal approach that integrates textual sentiment, emoji interpretation, and behavioral context to improve the detection of mental health conditions such as stress, anxiety, and depression. The system collects social media data and performs preprocessing to extract meaningful features from text and emojis. Advanced Natural Language Processing (NLP) techniques and deep learning models are used to analyze text, while emojis are mapped to emotional representations to enhance sentiment understanding. Behavioral features such as posting frequency and temporal activity patterns are also incorporated to capture psychological trends over time. These multimodal features are fused using a deep learning framework to classify users into different mental health categories. Experimental results indicate that the proposed approach significantly outperforms traditional text-only models by providing higher accuracy and better contextual understanding of user emotions. The system enables early detection and continuous monitoring, making it a valuable tool for mental health awareness and intervention. This research highlights the importance of leveraging multimodal data for more effective and reliable mental health analysis in digital environments.
CH. N. Padmavathi,P. VIGNESH, S. SUCHITHRA, R. BHUMIKA, P. SHASHIKANTH,