IES International Journal of Multidisciplinary Engineering Research
Welcome to the IES International Journal of Multidisciplinary Engineering Research (IESIJMER), where scientific research and innovation will meet in the future., IESIJMER a leading multidisciplinary platform, is committed to investigating the dynamic nexus of science and technology, creating an atmosphere that supports ground-breaking concepts and game-changing research. A Centre for Innovation IESIJMER acts as a hub for scholars, practitioners, and thought leaders in a world where technology innovation is changing industries and demarcating borders. In order to promote information sharing that spurs innovation and sustainable growth across various fields, we are dedicated to closing the gap between theory and practice. Our Objective At IESIJMER, we are strong proponents of teamwork and the ability of interdisciplinary research to tackle today's intricate problems. Our goal is to encourage and enable scientists to push the envelope of what is conceivable in order to make a significant, ethical, and sustainable contribution to the advancement of science and technology. What We Provide Cutting-Edge Research: Through original research papers, insightful reviews, and thought-provoking case studies, our journal covers the latest developments and trends in science and technology management. Global Reach, Local Impact: Because of its diverse and international audience, IESIJMER offers a platform for global perspectives while maintaining a focus on local problems and solutions. Open Access for everybody: We are committed to making research accessible to everybody. As an open-access journal, IESIJMER encourages transparency and information exchange by providing unrestricted access to all of our publications. Become a Part of Our Community Why IESIJMER is more than simply a journal; we are a vibrant community of innovators, educators, and leaders. We would be delighted to have you join us as we explore new frontiers in science and technology. Whether you are a researcher trying to publish your findings, an academic eager to stay up to date on the latest developments, or a practitioner looking for practical insights, IESIJMER is your gateway to a multitude of options. Examine the Future with IESIJMER The future is emerging at the intersection of science and technology. By working together, we can build a future where creativity is unhindered and science leads the way.
Medical Tracker App and Wearable Health Band
This system integrates a mobile-based medical tracker application with a wearable health band to deliver a comprehensive and proactive healthcare management solution. The mobile application acts as a centralized platform for managing medication schedules, tracking medicine supplies, and issuing timely refill alerts to ensure consistent treatment adherence. It also includes a smart lookup feature that uses real-time data to locate nearby pharmacies with the required medicines available, enhancing convenience and accessibility. The wearable health band complements this by providing continuous monitoring of vital parameters such as heart rate and blood pressure, along with an automatic fall detection and alert mechanism to ensure immediate response during emergencies. Together, these components create a seamless ecosystem that connects medication management, real-time biometric monitoring, and location-based assistance. This integrated approach not only improves patient compliance and emergency readiness but also promotes independent and safer health management, particularly benefiting elderly individuals and patients with chronic conditions.
Copyright (c) 2026 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
Robertanet: Enhancedroberta Transformer Based Model for Cyber Bullying Detection with Glove Features
Social media has become a vital platform for communication, but it also encourages harmful behaviours such as cyberbullying, trolling, and hate speech. Manual moderation is slow and expensive, making automatic detection essential. This work presents an enhanced RoBERTa transformer model combined with GloVe word embeddings to detect cyberbullying in tweets. The system is tested against various machine learning and deep learning models and achieves about 95% accuracy, along with high precision, recall, and F1 scores. Cross-validation further confirms its reliability. The results show that advanced transformer models supported by effective feature representation can provide a strong solution for detecting cyberbullying on social media. Beyond academic evaluation, such a system can be valuable in protecting vulnerable users, especially teenagers, from psychological harm. Moreover, the approach has potential to be adapted across multiple social platforms, making it a promising step towards safer digital communities.
AI Platform for Missing Children and Human Trafficking Detection
Human trafficking and child abduction remain critical global challenges due to fragmented data systems and the lack of real-time monitoring tools. Traditional search processes involving manual record verification and public alerts are often time-consuming and inefficient. This project proposes an AI-driven platform for Missing Children and Human Trafficking Detection, integrating facial recognition, pattern analysis, and natural language processing (NLP) to enable proactive victim identification. The system unifies multiple data sources — including CCTV feeds, FIR records, travel logs, and social media intelligence — into a centralized web portal. Using a VGG-Face-based Convolutional Neural Network (CNN) for deep facial feature extraction and a K-Nearest Neighbor (KNN) classifier for efficient matching, the platform automatically identifies missing individuals or traffickers across surveillance networks. Once a match is found, the system sends real-time alerts to concerned authorities and families. The model demonstrates high accuracy even under variations in lighting, pose, and image quality. The proposed framework enhances the speed, accuracy, and scalability of investigations, offering an effective AI-assisted approach for law enforcement and social welfare agencies. Future extensions include mobile integration, predictive route analytics, and national-level deployment for cross-border trafficking prevention.
AI-Powered Android Application for Fruit and Vegetable Quality Detection
The proposed project introduces a mobile app that helps people check the quality of fruits (like apples, bananas, and oranges) using artificial intelligence. The app works on Android phones and uses a powerful deep learning model called EfficientNetB5. It looks at pictures of fruits and tells whether they are “Good”, “Bad”, or “Mixed” (a mix of both good and bad signs). Users can take a photo or upload one in the app, and it will quickly check the fruit’s freshness. This is useful for both customers and shopkeepers. It helps people choose better fruits and avoid waste. For shops and food companies, the app offers a cheap and smart way to sort out bad fruits, and even allows robotic help in the future. This app shows how smart technology can make daily life easier, help the environment by reducing food waste, and support better decisions when buying or selling fruits and vegetables.
Real-Time Automatic Speech Recognition Using Deep Learning
Real-time speech recognition has evolved dramatically with the introduction of deep learning architectures, enabling high accuracy, low latency, and robust performance across diverse acoustic conditions. This paper provides a comprehensive review and proposed framework using state-of-the-art models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformers, and end-to-end architectures like DeepSpeech and wav2vec 2.0. A complete system workflow, block diagrams, algorithmic steps, results, and conclusions are also presented. These models enable efficient parallelization, improved context modeling, and robust performance under real-world noise conditions, making them suitable for applications such as AI assistants, streaming transcription services, conversational AI, navigation systems, and edge-deployed embedded devices. Despite these advancements, achieving real-time performance remains challenging due to factors such as inference latency, memory footprint, streaming complexity, and the difficulty of processing long utterances in low-resource environments. This paper presents a comprehensive study of state-of-the-art deep learning architectures for real-time Automatic Speech Recognition (ASR), highlighting their design principles, computational characteristics, model variants, and deployment considerations. A detailed analysis of Conformer and RNN-T based streaming systems is provided, along with illustrations, data flow diagrams, and experimental insights. The paper also discusses ongoing challenges including multilingual adaptation, noise robustness, and on-device model optimization and outlines future research directions toward more efficient, scalable, and human-level real-time speech recognition systems.
Eye Disease Detection Enhancement Using a Multi-Stage Deep Learning Approach
Eye disease detection enhancement using a multi-stage deep learning approach introduces a lightweight and multi-stage deep learning framework for the automatic detection of eye diseases using retinal OCT and fundus images. The proposed model is designed to achieve a balance between accuracy and computational efficiency, making it ideal for real-world deployment, particularly in resource-limited clinical environments. The system begins with a robust preprocessing phase that normalizes and enhances the input images while addressing issues related to rotation, translation, and illumination variations. This step ensures that the model remains invariant to positional and intensity distortions, thereby improving its robustness and consistency across diverse imaging conditions. The architecture of the model is composed of three sequential stages that work together to deliver highly accurate diagnostic predictions. In the first stage, fine-grained spatial and textural features are extracted from retinal images, allowing the network to capture subtle structural variations that often indicate early disease progression. The second stage integrates a dual-branch structure that combines convolutional feature blocks with identity mapping branches. This design helps the network simultaneously learn both low-level detailed features and high-level semantic representations, enabling a deeper and more comprehensive understanding of retinal abnormalities. The extracted features from both branches are then fused to retain discriminative information and eliminate redundancy before being passed into the final stage. In the classification stage, the merged feature representations are processed through fully connected layers and a softmax-based output unit to generate precise disease predictions. This multi-stage feature integration improves the model’s discriminative power and enhances its overall accuracy compared to conventional deep learning approaches. Experimental evaluations conducted on three benchmark datasets OCT 2017, Dataset-101, and Retinal OCT C8 demonstrated that the proposed model consistently outperforms existing architectures in terms of accuracy, precision, and recall. The results confirm that the combination of spatial, frequency, and contextual features contributes to better generalization across different types of retinal disorders. Moreover, the lightweight nature of the network significantly reduces computational load and memory usage without compromising performance. This efficiency makes the system particularly suitable for clinical environments with limited computational infrastructure, such as portable diagnostic devices or remote screening setups. Overall, the proposed multi-stage deep learning framework represents a powerful and efficient solution for automated eye disease detection. It enhances diagnostic reliability, supports early disease identification, and provides a scalable pathway toward integrating artificial intelligence into ophthalmic care and decision-support systems.
Sentiment Analysis on Social Media Using Deep Learning Techniques
Social media platforms generate vast amounts of opinion-rich data that reflect public emotions and attitudes. The project explores sentiment analysis using deep learning models to classify emotions in user-generated text. The system achieves high accuracy in identifying emotional polarity, helping interpret public opinion effectively. The project uses YouTube Data API, social media posts to extract comments from video links posted on social media. Comments are cleaned and passed through a multilingual sentiment analysis model based on BERT to classify emotions. The model supports both English and regional language inputs, including code-mixed text. Visual results are displayed as pie charts along with a comment-wise sentiment summary. The tool enables real-time sentiment extraction from video discussions using deep learning.
Diabetic Retinopathy Detection Using Machine Learning Techniques
Diabetic Retinopathy (DR) is an eye disease, occurs because of diabetes that makes lesions in the retina and affect vision. If it is not noticed early, it will be led to blindness. If DR could find out early and get treated, the vision loss can be avoided. Manual diagnosis process takes long time, more cost effective and it is more complicated. Therefore, Computer Aided Diagnosis Systems can be used to reduce cost, time, and other serious complications. Many studies are taking place in this area, by using the Traditional Machine Learning Methods like Support Vector Machine (SVM), Random Forests (RF), Logical Regression (LR) etc. The researchers are now a days focusing to keep the security and privacy and to increase the efficiency and accuracy, hence Federated Learning (FL) is a good method for this. FL ensures Patient’s data Security and Privacy and also ensure accuracy of the Result.
Managing crypto investments for retail investors is often hindered by high volatility, poor timing (buying at peaks and selling at lows), and the inherent risks of centralized platforms. This project introduces a decentralized, automated SIP model for crypto investments, offering a non-custodial and multi-asset investment protocol to limit these challenges. The system automates crypto investing like a Systematic Investment Plan (SIP). All SIP rules (amount, frequency, maturity) are enforced automatically by smart contracts, ensuring trustless and transparent execution. Users maintain full custody of their funds in non-custodial wallets like MetaMask, and investments are made directly using stablecoins (USDT/USDC) into crypto pools (BTC, ETH, SOL, BNB). The purchased assets are stored in a smart contract vault until maturity, promoting structured long-term investing and verifiable on-chain transparency. By leveraging smart contracts and dynamic frequency validation, the system provides a consistent, reliable, and non-custodial solution for long-term wealth building in the decentralized Web3 space.
AI-Powered Food Recognition and Nutritional Analysis via Deep Learning
People today are becoming more health-conscious and want to keep track of what they eat. However, many struggle because they don’t have the right tools to identify or analyze their food accurately. Manual tracking can be time-consuming and unreliable, making it hard to maintain a healthy diet. To solve this, we have proposed a new system using artificial intelligence and image recognition technology. This system will make food identification faster and more accurate. With AI-based food recognition, users can simply take a picture of their meal and get instant details such as food type and nutritional information. This helps people monitor their eating habits and make better health choices. Unfortunately, many users and organizations are still unaware of how this technology works or how it can benefit them in daily life.
AI Powered Voicemail Prioritisation System
Managing voicemails in critical sectors such as healthcare, finance, and customer service is often hindered by delays, inefficiencies, and missed urgent messages. This project introduces an AI-powered voicemail system designed to intelligently capture, analyze, and prioritize voice messages, ensuring that high-urgency cases are addressed immediately. Using Twilio for voicemail capture and Google SpeechRecognition for transcription, the system combines text and audio analysis to assess both semantic content and vocal tone. BERT-based text classification evaluates urgency and routes messages to the appropriate department, while OpenSMILE and Random Forest models analyze acoustic features to detect stress, pitch, and speed, indicating urgency levels. PII is automatically redacted using SpaCy to maintain privacy and data security. By merging speech processing, NLP, and machine learning, the system offers a scalable, reliable, and efficient solution for modern communication management.
PROPCHAIN: A Blockchain-Based Real Estate Platform
The real estate sector faces numerous challenges, including lack of transparency, fraudulent transactions, and delays in property registration. Traditional land record systems are often paper-based and centralized, making them vulnerable to tampering and data loss. To address these issues, a blockchain- based real estate management system has been proposed. This system ensures secure and tamper-proof property records by leveraging the decentralized nature of blockchain technology. Smart contracts are used to automate property transactions, reducing the need for intermediaries and minimizing human error. Every transaction is stored permanently on the blockchain, ensuring transparency, traceability, and trust among all parties involved. By eliminating middlemen and enhancing security, the proposed system creates a more efficient and reliable platform for property buying, selling, and ownership transfer.
Disease detection by artificial intelligence introduces the importance of AI ,convolutional neural networks(CNNs) in medical image analysis for diseases especially lung cancer.Early disease detection,accuracy and consistency,time efficiency,scalability,improving healthcare outcomes are the primary need of the project.Developing a machine learning model specifically by CNNs to detect and classify lung cancer from medical images.Medical images,preprocessing techniques,feature extraction,machine learning model are methods are used here.Use of Convolutional Neural Networks,image processing techniques etc innovative approaches in this project.The key outcomes of the project is high accuracy,automated diagnosis,improved healthcare outcomes.This project "Disease Detection By Artificial Intelligence" demonstrates potential of machine learning in automating disease detection using medical images by achieving an accuracy of 94.297%
Copyright (c) 2025 IES International Journal of Multidisciplinary Engineering Research https://creativecommons.org/licenses/by-nc-sa/4.0
Dysgraphia Detection Using Deep Learning
Dysgraphia is a neurobiological disorder that impairs an individual's ability to write, often resulting in unclear and irregular handwriting, reduced writing speed, and difficulties with pencil grip and spacing. Early detection and accurate classification of these disabilities are essential for effective intervention and support. Traditional assessment methods, including manual evaluations lack the precision needed for reliable diagnosis, leading to inconsistent identification of learning disabilities. Here, the proposed system is based on deep learning for the detection and classification of learning disabilities through handwriting analysis. Utilizing a dataset of handwritten samples from children, the model employs advanced Convolutional Neural Network (CNN) architectures and Vision Transformers, to accurately identify and classify handwriting patterns indicative of dysgraphia. By leveraging these sophisticated models, this proposed system provides timely and precise diagnoses, enabling tailored educational strategies and interventions for affected children, ultimately enhancing their learning outcomes and quality of life.
Password Based Smart Door Lock System for Enhanced Security
In this progressively insecure world today, there is indeed no going back into using old-fashioned keys and locks. That is because keys can get lost, stolen, or even duplicated and leave homes or offices vulnerable. This is why this project aims at building a Smart Doorlock System that uses a password instead of a key, simple and added security. In fact, this entire system would be powered by a small yet very sturdy microcontroller known as the ESP8266. It can do the locking process alone, and it is also base to future online access system. For example, in this manner, to unlock the door, the user needs to enter a password into the keypad. If the password is correct, a relay switches on a solenoid lock and the opening of the door takes place; if incorrect, the door is locked always by the system-and we can even add some features like alarms or mobile alerts later. A small LCD display would also be used for messaging purposes, such as "enter password" and "access denied," which would keep the user updated on the occurrence in the system. It is cost-effective and extremely easy to build, making it an irresistible option for anyone who wishes to increase the safety of their residence or experiment with technology toward a smart home. Overall, smart lock project combines security, simplicity and future promises. This is a modern way of keeping things under lock and key, and not having to carry keys around anymore. This can be a home, office, or even a school locker.
In the context of a more linked and globalized society, the significance of proficient cross-cultural communication has been increasing to a position of utmost importance. Language functions as a crucial medium that establishes connections among people, corporations, and countries, demanding the implementation of precise and effective translation systems. This comprehensive review paper aims to contribute to the evolving landscape of AI-driven language translation by critically examining the existing literature, identifying key debates, and uncovering areas of innovation and limitations. The primary objective is to provide a nuanced understanding of the current state of AI-driven language translation, emphasizing the advancements, challenges, and ethical considerations. In this review, ongoing debates surrounding AI-driven language translations were actively involved. By evaluating different viewpoints and methodologies, insights into unresolved questions that contribute to a broader discourse in the field were provided. The future trajectory of this study involves the incorporation of cross-lingual dialect adaptability and the advancement of Artificial Intelligence translation systems, with a focus on prioritizing inclusion and cultural understanding.
It is widely acknowledged that traditional agricultural practices must evolve to meet the growing global demand for food, all while addressing critical challenges such as water scarcity and the adverse effects of climate change. The urgency for environmentally sustainable and resource-efficient agricultural methods has never been greater. In response to these demands, IoT-based Smart Agriculture has emerged as a promising and innovative approach that integrates advanced technology with conventional farming. One notable application within this domain is the Smart Irrigation System, which leverages IoT to optimize water usage in agricultural fields. By using soil moisture sensors, the system continuously monitors soil conditions and automatically adjusts irrigation schedules based on real-time data, ensuring efficient and targeted water distribution. This minimizes water wastage, maintains ideal soil moisture levels, and ultimately supports better crop growth. The system is controlled through a user-friendly web application that allows farmers to monitor and manage irrigation remotely, compare IoT-generated data with manually input values, and review detailed irrigation history for informed decision-making. In addition to conserving water, the Smart Irrigation System reduces labor costs and enhances overall productivity, making it a powerful tool in advancing sustainable agriculture. This project highlights the transformative potential of IoT in fostering smart farming practices and addressing the pressing environmental and resource-related challenges faced by modern agriculture.
Traditional shopping systems relying on barcode scanning often encounter inefficiencies, such as long checkout queues and errors in manual scanning. Missing or damaged barcodes slow down the process, while customers face inconveniences such as removing items from their carts for scanning. These issues, combined with the increasing need for streamlined retail operations and enhanced customer convenience, highlighted the necessity for a modern, automated solution. The problems with existing methods served as the driving force behind the development of the RFID-based Smart Shopping Cart system. This innovative project leverages RFID technology to automate item detection and billing processes in real time. By integrating RFID readers, microcontrollers, and LCD displays onto shopping carts, the system eliminates the need for manual scanning. The cart provides real-time updates on product details and costs, ensuring speed and accuracy throughout the shopping experience. Additionally, this system addresses critical challenges like customer adaptation to new technology and ensuring reliable communication between system components to enhance usability. The RFID-based Smart Shopping Cart offers a transformative approach to shopping, minimizing human effort and improving retail efficiency. While challenges such as network connectivity and data security persist, this system's advantages, including faster checkout times and improved inventory management, make it a significant step forward in retail innovation. This system not only solves existing problems but also sets a foundation for further advancements in automated retail technologies.
Cervical Spondylosis Detection Band with IoT Integration
This project presents a cost-effective cervical health monitoring system using Wireless Body Area Networks (WBANs) and IoT technology. The neckband device, equipped with gyroscope and posture sensors, detects improper neck posture in real time and sends alerts to encourage correction. Data is transmitted to a cloud server for analysis, and machine learning is used to improve detection accuracy. Designed for continuous monitoring, the system aims to prevent cervical spondylosis, offering an accessible solution especially beneficial for children and the elderly.
Monitoring and caring for plants can be a challenging and time-consuming process due to traditional methods lacking automation, which results in inefficient water usage and inconsistent care routines, ultimately affecting plant growth and health. Manual watering often leads to over or under-watering, exacerbating these issues and making plant care labour- intensive and less effective. The project proposes a automatic plant watering system utilizing IoT technology, including sensors and automated watering mechanisms, to provide a user-friendly platform for plant care. This system collects real-time data on soil moisture, temperature, and humidity, and automates watering through a water pump controlled by a relay. By ensuring optimal water usage and consistent care, this solution aims to promote healthier plant growth and simplify plant management for individuals. Additionally, the system can be integrated with mobile applications to offer remote monitoring and control, providing users with real-time notifications and insights into their plant’s needs. This innovative approach not only enhances the efficiency of plant care but also makes it more accessible to a broader audience, including those with limited gardening experience.
Brain Tumor Detection Using Deep Learning
Brain tumors, arising from abnormal cell growth in the brain, can be life-threatening without accurate detection and treatment planning. Early detection and accurate classification of brain tumors are crucial for effective treatment planning and improved patient outcomes. Traditional methods like manual MRI examination and rule-based algorithms often lack precision, resulting in inconsistent detection and classification of brain tumors. In this work, the brain tumor detection and classification system is proposed to resolve these problems. This model uses CNN to accurately detect and classify brain tumors into types such as glioma, meningioma, and pituitary tumors. Using a dataset of 7,022 brain MRI images, the model utilizes advanced CNN architectures, including ResNet-50, DenseNet, and EfficientNetB1 for accurate detection and classification of tumors. However, these models tend to suffer from overfitting issues, affecting their generalization capability. To address this, we also employ InceptionV3, which demonstrates superior accuracy and robustness in detecting and classifying brain tumors. Therefore, patients will receive an accurate diagnosis, and the necessary treatment plan will be developed.
Smart Waste Bin is an IoT based solution designed to improve waste management efficiency in urban areas. Traditional waste collection methods often result in overflowing bins, leading to environmental and health hazards. To address this, Smart Waste Bin integrates proximity sensors for touchless operation, automatic lighting for better visibility, and ultrasonic or infrared sensors to monitor real-time waste levels. The collected data is transmitted to an IoT platform, which notifies authorities when the bin reaches capacity. By optimizing waste collection routes, this system reduces fuel consumption, lowers operational costs, and supports sustainability, aligning with smart city initiatives.
Experimental Investigation of Concrete Enhanced with Biochar
The construction industry is a major contributor to global carbon emissions, primarily due to cement production. To address this challenge, this study explores the incorporation of biochar, a carbon-rich byproduct of biomass pyrolysis, into concrete as a sustainable alternative. The biochar used in this research was derived from coconut shells and processed to pass through a 150-micron IS sieve to ensure uniformity in the concrete mix. The primary objective of this study is to evaluate the mechanical and durability properties of biochar-enhanced concrete while assessing its environmental impact. The methodology involved preparing concrete mixes with varying biochar replacement ratios (0%, 5%, 10%, and 15%) and conducting a series of tests after 7, 14, and 28 days of curing. Compressive strength tests were performed to analyze the structural integrity, while durability assessments, including carbonation resistance, sulfate attack resistance, and water absorption tests, were conducted to evaluate the long-term performance of biochar concrete. Additionally, a carbon footprint analysis was performed to quantify the environmental benefits of biochar incorporation. The results indicate that biochar-modified concrete exhibits improved mechanical and durability characteristics at optimal replacement levels. The porous nature of biochar enhances hydration, contributing to strength development and improved moisture retention. The carbonation and sulfate resistance tests demonstrated that biochar-concrete is more resilient against environmental stressors compared to conventional concrete. Furthermore, the carbon footprint analysis revealed that replacing cement with biochar significantly reduces CO2 emissions, promoting sustainable construction practices. This research highlights the potential of biochar as an eco-friendly material that not only enhances concrete properties but also contributes to waste management and carbon sequestration. By integrating biochar into concrete, this study provides a viable solution for reducing the environmental footprint of the construction industry while maintaining structural performance.
Integrated Fire Detection, Seat Belt Release and Door Unlocking System for Enhanced Vehicle Safety
Our project deals with the design and the fabrication of the INTEGRATED FIRE DETECTION, SEAT BELT RELEASE AND DOOR UNLOCKING SYSTEM FOR ENHANCED VEHICLE SAFETY. We presents an revolutionary fireplace detection and protection system for automobiles aimed toward improving passenger safety throughout fire-related emergencies. The gadget integrates fireplace detection, automatic seatbelt elimination, and door unlocking mechanisms to allow speedy evacuation. Fireplace detection sensors constantly monitor for signs and symptoms of fireplace, inclusive of smoke or abnormal temperature increases. Upon detection, the gadget mechanically disengages seatbelts and unlocks car doorways to make certain that passengers can speedy go out the vehicle without being trapped through seat restraints or locked doorways. The system is designed to operate autonomously, decreasing reaction time and doubtlessly saving lives in crucial conditions. Additionally, it may be included with existing vehicle protection systems, enhancing normal protection and emergency preparedness in contemporary automobiles.
The electrical cashew nut roasting machine is an advanced solution designed to enhance the efficiency, quality, and sustainability of cashew nut processing. Traditional roasting methods using firewood, charcoal, or gas are labor-intensive, inconsistent, and environmentally harmful. This machine operates on electricity, providing uniform heat distribution and automated roasting, ensuring optimal quality and flavor of the nuts. Constructed with a stainless steel roasting vessel , an electric heating element, a rotating vessel . the machine ensures even roasting while minimizing energy consumption. The automated process reduces labor requirements and eliminates the need for constant monitoring. The electrical cashew nut roasting machine is energy-efficient, cost-effective, and environmentally friendly, reducing carbon emissions and deforestation associated with traditional roasting. Its durable, easy-to-maintain design enhances longevity, making it a valuable investment for small and large-scale producers. Moreover, its energy-efficient design helps reduce production costs, making it a viable investment for small-scale and large-scale producers. This machine significantly improves the productivity, profitability, and market value of cashew nuts, revolutionizing the industry with its precision, convenience, and sustainability.
Microplastic Filtration from Waste Water Using Silica
Microplastic contamination in wastewater poses significant environmental and health risks, particularly when treated water is used for irrigation. This study explores the efficiency of silica as a filtration medium for removing microplastics from wastewater and assesses the suitability of the treated water for irrigation. Various silica-based filtration techniques were analyzed, considering factors such as particle size, adsorption capacity, and filtration efficiency. The effectiveness of silica in reducing microplastic concentration was evaluated using microscopic and spectroscopic analyses. Additionally, physicochemical parameters of the treated water, including pH, turbidity, and nutrient content, were assessed to determine its impact on soil and plant health. The results indicate that silica-based filtration can effectively reduce microplastic levels, making the treated water safer for irrigation while maintaining essential nutrients. This study highlights the potential of silica as a cost-effective and sustainable solution for microplastic removal in wastewater treatment.
Cloud Based Intravenous Drip Monitoring System
The Cloud-Based Intravenous (IV) Drip Monitoring System is a next-gen healthcare solution designed to enhance accuracy, safety, and efficiency in clinical IV therapy. Unlike traditional manual monitoring, which is prone to human error and inconsistency, this system uses smart IoT integration to automate the process. With real-time sensors, it tracks the drip rate, fluid level, and infusion status, transmitting the data wirelessly to a cloud platform. Healthcare professionals can remotely access this information through mobile and web applications, enabling them to monitor and adjust parameters as needed. The system sends instant alerts when irregularities occur, ensuring timely intervention. It also logs a detailed infusion history, aiding in treatment analysis and optimization. By reducing manual workload and enhancing patient safety, this system makes IV therapy more reliable and efficient, especially in high-demand settings like hospitals and emergency care units.
Accident Detection Through CCTV Surveillance
Timely accident detection and emergency response play a crucial role in reducing fatalities and mitigating injuries caused by road accidents. Traditional surveillance systems primarily rely on manual monitoring, which is often inefficient in detecting accidents in real time. "Accident Detection through CCTV Camera Surveillance" presents an AI-powered approach to automate accident identification using deep learning techniques. The system utilizes “Mask R-CNN” and “YOLO v8” to analyse live CCTV footage, accurately detecting accidents and differentiating them from regular traffic events. Once an accident is identified, automated alerts are generated and dispatched to emergency services, including ambulance providers, fire departments, and law enforcement agencies. By minimizing human dependency and response delays, this solution enhances the efficiency of emergency management systems. The proposed framework integrates artificial intelligence with real-time surveillance, providing a scalable and cost effective accident detection mechanism. This research aims to improve road safety, optimize emergency response times, and reduce the loss of life due to delayed medical and rescue assistance. The implementation of this system can significantly contribute to the development of smart cities, reinforcing AI's role in intelligent traffic monitoring and public safety enhancement.
Study on the effect of soil properties in the presence of plastic waste
This study investigates the impact of plastic waste on the properties of soil, focusing on its effects on physical and chemical characteristics. With the increasing accumulation of plastic waste in landfills, its interaction with soil has become a significant concern for environmental and engineering practices. The research aims to conduct a comparative analysis of soil samples with varying concentrations of plastic waste, evaluating changes in parameters such as permeability, compaction, strength, water retention, levels. Laboratory tests, including Atterberg limits, Proctor compaction, Permeability tests, specific gravity test, moisture content, particle size distribution, ph., EC, Organic matter and un- confined compressive strength test were performed on soil mixed with different types and quantities of plastic materials (e.g., polyethylene, polypropylene). The findings reveal that the incorporation of plastic waste leads to altered soil behavior, which may either improve or degrade soil properties depending on the type of plastic and its concentration. This study provides essential insights into how plastic waste impacts soil's physical stability, offering potential implications for soil stabilization techniques, soil remediation methods, and sustainable engineering practices.
Stability Analysis of Landslide Susceptible Areas Using PLAXIS 2D
Landslide occurrences are largely controlled by different causative factors. Geotecnical properties of soil viz-a-viz type of soil, density, cohesion, angle of internal friction, hydraulic conductivity, etc. affect the natural stability of slopes and therefore, these data can be utilized to determine the susceptibility of a slope to landslides. In this project, an attempt is made to analyze the stability of slopes especially in landslide prone areas in the Thrissur district. For this, three different areas are selected viz-a-viz, Vilangan hills, Kuranchery and Akamala in Kerala. These study areas have experienced several slope failures in the past few years. With the help of soil data from laboratory experiments, rainfall data of the study area, and slope stability analysis using PLAXIS 2D software, the factor of safety of slopes in these study areas are obtained. Software modelling can provide analytic frameworks for quantifying and understanding the underlying patterns of landslides under various local conditions. The results obtained from stability analysis, is useful in categorizing study areas into different classes based on their susceptibility to landslides. Such a study would be helpful for future land use planning and landslide mitigation in the study areas.