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.
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%
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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.
Integrated Planning and Development of KSRTC Bus Terminal Thrissur
This project focuses on the comprehensive planning, design, and structural analysis of a modern, sustainable bus terminal to enhance urban mobility and transportation efficiency. The proposal integrates essential infrastructure, including a fully functional terminal building, dedicated parking zones for buses, cars, and bikes, and a pedestrian-friendly walkway connecting the terminal to the railway station for seamless multimodal connectivity. The objective is to create a user-friendly, structurally sound facility that ensures optimal traffic flow and passenger convenience. Structural analysis ensures safety and compliance with IS codes, while quantity surveying and cost estimation assess material requirements and feasibility. Preliminary findings show that the integrated design improves space utilization, reduces congestion, and enhances the passenger experience. By incorporating sustainability and structural optimization, the project offers a cost-effective, efficient, and future-ready transport hub for Thrissur.
Clayey Soil Stabilization Using Rice Husk Ash & Perlite
Clayey soils, characterized by their high plasticity and low strength, pose significant challenges in construction and geotechnical engineering. This study investigates the efficiency of rice husk ash (RHA) and perlite as sustainable stabilizers for improving the engineering properties of clayey soils. Rice husk ash, an agricultural by-product, and perlite, a volcanic glass, were utilized in varying proportions to treat the soil. A comprehensive suite of laboratory tests, including Atterberg limits, Light compaction, unconfined compressive strength (UCS), was conducted to evaluate the mechanical and physical properties of the stabilized soil. The results demonstrated that the addition of RHA and perlite significantly enhanced the soil’s strength and reduced its plasticity. The optimal mix of 9% RHA and 8% perlite yielded the highest UCS values, indicating a substantial improvement in load-bearing capacity and stability. The formation of cementitious compounds, such as calcium silicate hydrate (C-S-H), during addition of RHA and Perlite contributed to the soil’s enhanced strength and durability. The study concludes that the combined use of RHA and perlite is an effective, eco-friendly, and cost-efficient method for clayey soil stabilization, offering a promising alternative to traditional stabilizers like lime and cement. This approach not only addresses the challenges of weak clayey soils but also promotes the sustainable utilization of industrial and agricultural waste materials.
Combined Effect of Glass Powder & Sisal Fiber on Strength of Concrete
The incorporation of sisal fiber and glass powder into concrete has been investigated to evaluate their combined effect on the mechanical properties and overall strength of concrete. Sisal fibers, known for their high tensile strength and natural availability, provide reinforcement to the concrete matrix, enhancing its ductility and toughness. Glass powder, on the other hand, is a waste byproduct of glass production, and its use in concrete not only promotes sustainability but also contributes to improved compressive strength due to its pozzolanic properties. This study explores the synergistic effects of combining both materials in varying proportions and examines how the inclusion of sisal fiber and glass powder influences concrete’s compressive, tensile, and flexural strength. Results indicate that the optimal combination of sisal fiber and glass powder leads to a significant improvement in the mechanical performance of concrete, as it combines the benefits of increased strength, durability, and sustainability. The findings of this research support the potential of using these materials as an effective means to enhance the performance of concrete in construction applications, while also addressing environmental concerns by utilizing industrial waste products.
The Smart Ration Distribution System (SRDS) represents an innovative approach to enhancing the transparency, efficiency, and accessibility of public ration distribution. Leveraging cutting-edge technologies such as the Internet of Things (IoT), cloud computing, and real-time data analysis, the SRDS enables the fair and effective allocation of essential goods without depending on traditional RFID or GSM methods. The system employs smart ration cards connected to a centralized database, providing real-time tracking of entitlements, consumption patterns, and stock levels. At distribution points and storage facilities, sensors are installed to automate stock monitoring, ensuring timely replenishment and minimizing the risk of shortages. The inclusion of data analytics further allows for the detection of discrepancies, reducing fraudulent practices, and improving system transparency. A user-friendly platform empowers beneficiaries to check their entitlements and ration status while offering a quick resolution mechanism for grievances. By automating processes and eliminating intermediaries, SRDS improves the efficiency of ration distribution, reduces delays, and guarantees that essential goods reach those who need them fairly and without hindrance.
Web vulnerability scanners play a critical role in cybersecurity by identifying security weaknesses in web applications through automated testing. These tools detect vulnerabilities such as SQL injection, cross-site scripting (XSS), and security misconfigurations by simulating real-world attacks. Their effectiveness varies based on detection algorithms, scan depth, and adaptability to modern web technologies. This study evaluates the strengths and limitations of these scanners, emphasizing their integration into the software development lifecycle for proactive security management. By leveraging these tools effectively, organizations can mitigate risks and enhance web application security against evolving threats.
Market Trend Analysis Using Deep Learning
A novel approach to market trend prediction through the analysis of video advertisements using artificial intelligence (AI). The proposed software leverages advanced computer vision and natural language processing (NLP) techniques to extract and interpret key elements from video ads, such as visual motifs, textual content, and sentiment. By analyzing patterns in ad content, targeting strategies, and consumer engagement, the AI system can identify emerging market trends and predict future consumer preferences. The software integrates machine learning algorithms to analyze historical ad data and realtime inputs, offering actionable insights into shifting market dynamics and advertising effectiveness. This approach provides businesses with a data-driven tool for anticipating market shifts, optimizing advertising strategies, and enhancing competitive positioning.
In real estate and interior design, the ability to visualize furniture layouts, room modifications, and various design options within a real-world context can significantly enhance user experience and decision-making. Traditional methods of spatial planning are time-intensive and require extensive resources, making it difficult for clients to fully comprehend design choices before implementation. To address these challenges, the Reality Home AR app offers an immersive and accessible solution using advanced Augmented Reality (AR) technology. Leveraging platforms like AR Core and ARKit, the application provides users with real-time interactions, enabling them to place virtual furniture, adjust colours, and explore 3D property layouts directly within their physical environment via smartphone. The app detects real-world surfaces, such as floors and walls, with high accuracy, allowing for stable and realistic positioning of AR objects, which enhances user confidence in spatial planning decisions. Users can easily alter interior designs by changing colours, rearranging furniture, and examining room dimensions through an intuitive interface. Beyond visualization, Reality Home AR enables users to modify space elements interactively, which reduces the time and resources needed to finalize layouts and allows for greater personalization. Designed for both iOS and Android, Reality Home AR is compatible with a wide range of devices, broadening its accessibility. Future enhancements could incorporate features like depth-sensing for improved spatial accuracy or support for wearable devices to further heighten the immersive experience. This platform thus presents a transformative approach to real estate and interior design by enabling users to visualize, personalize, and ultimately make informed decisions about their spaces. Reality Home AR not only redefines user engagement but also promotes efficient, accurate, and user centred design practices for the modern era.
AUTODEVOPS: AI-Driven Software Development & Deployment Automation
In traditional software development, the process of translating project requirements-into a functional design, writing code, and creating test cases is often labour-intensive,time-consuming, and prone to human error. These challenges can lead to delays, inconsistencies,and increased costs, particularly in complex projects. Addressing these issues,we propose a Generative AI system that revolutionizes the software development life cycle by automating critical stages. This system begins by employing advanced Natural Language-processing (NLP) to analyse project requirements, extracting essential information to generate a comprehensive project design, including architecture diagrams and class structures. Following the design phase, the system autonomously generates code tailored to the identified design patterns and suggests deployment steps. Furthermore, the system automatically produces detailed test cases in a structured table format, ensuring thorough and consistent validation of the software.This Generative AI system enhances the efficiency,accuracy, and reliability of software development, addressing common challenges and paving the way for more streamlined and effective software engineering practices.
Azima 3.0 is a revolutionary robotic system for changing the face of advertising, communication, and automation in highly visited public spaces. With improved robotics and artificial intelligence, there has been high demand for intelligent interactive systems Azima 3.0 meets this need by merging high-definition display technology, face recognition, and remote control into a very interactive and dynamic enterprise and organizational platform. One of the standout features of Azima 3.0 is its 10.5-inch touchscreen screen, which can be operated remotely through a smartphone or computer. This functionality allows for real-time content updating, and as such, it is an effective advertising, event promotion, and public information delivery tool. The robot also has a face recognition sensor and high-definition camera, making it useful for meeting attendance tracking, security surveillance, and interactive customer interaction. The platform combines several state-of-the-art technologies, such as Raspberry Pi 3 Model B, Arduino, ESP8266 WiFi module, LED matrix displays, and motor driver modules. They provide smooth movement, real-time content management, and improved user interaction. Intended for mounting in airports, malls, exhibitions, metro stations, and other crowded areas, Azima 3.0 offers businesses a cost-effective and interactive alternative to conventional static advertising methods. Apart from business applications, the robot also holds promise for use in schools, business environments, and public service. It can be employed to assist with automated registrations, confirmation of attendance, and tailored customer service. It can be used to help with automated registrations, attendance confirmation, and customized customer service. However, issues such as power consumption, visibility of the display, and integration with software need to be resolved in order to provide maximum performance. Azima 3.0 is a move towards the future of smart robotic systems, illustrating how automation and digital marketing can be merged into one cohesive package to drive public interaction and productivity.
Dress pattern recognition is a critical task in fashion technology and digital retail that involves identifying and categorizing various clothing patterns automatically. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image recognition tasks due to their ability to learn hierarchical patterns and features from visual data. This paper presents a novel approach to dress pattern recognition using a CNN algorithm. The proposed system utilizes a deep learning framework to classify different dress patterns such as stripes, polka dots, floral, and checks. The CNN model is trained on a diverse dataset of dress images collected from various online fashion platforms, ensuring a wide range of pattern types and styles. Key components of the approach include preprocessing techniques for image normalization and augmentation to enhance the model's robustness against variations in lighting, scale, and orientation. The network architecture is optimized through hyperparameter tuning and the use of advanced techniques like dropout and batch normalization to prevent overfitting and improve generalization. Experimental results demonstrate that the CNN-based model achieves high accuracy in dress pattern recognition, outperforming traditional machine learning methods. The study concludes that the integration of CNNs in fashion analytics can significantly enhance the efficiency of digit u al cataloging and personalized shopping experiences. Future work will explore the application of transfer learning and further refinement of network architectures to improve performance across even more complex pattern types.
IOT-Based Manhole Detection and Monitoring System
The IoT-based manhole detection and monitoring system is an innovative solution that aims to enhance public safety by monitoring the condition of manholes in real time. The proposed system consists of sensors inside the manholes and can detect various parameters. This project focuses on developing a sensing system for manholes, incorporating temperature, gas, water level, and tilt sensors. These sensors provide real time data to ensure safe working conditions, detect hazardous gases, monitor water accumulation, and assess structural stability. The integrated system enhances infrastructure management by enabling remote monitoring and timely interventions, thereby improving safety and operational efficiency in urban environments. The sensor data is transmitted through IoT technology to a digital device. Hence, the system has the potential to reduce the risk of accidents and improve the efficiency of maintenance tasks by providing valuable insights into the condition of the manholes. The implementation of this system significantly improves safety by allowing for early detection of hazardous conditions and preventing potential accidents. Additionally, it optimizes maintenance schedules and resource allocation, leading to cost savings and more effective urban infrastructure management.
An AI-powered travel planning application has been developed to simplify and enhance the process of organizing personalized trips. Utilizing machine learning algorithms and extensive datasets, the application curates tailored itineraries based on user preferences such as budget, travel dates, destination, and group size. By integrating with real-time flight and accommodation databases, it ensures users receive the most relevant and up-to-date travel options. The AI continuously refines recommendations by analyzing user behavior and historical travel data, improving the accuracy and personalization of itineraries over time. Additionally, the app features seamless integration with geolocation services and third-party APIs, allowing users to book flights, hotels, and activities directly within the platform. This advanced approach not only streamlines trip planning but also adapts to the evolving needs of modern travelers, providing a convenient and intelligent solution for both occasional and frequent travelers alike.
Data security is vital in forensic investigations, where data integrity and provenance are essential. Blockchain, specifically on the Ethereum platform, offers a decentralized and tamper-resistant solution, ensuring forensic data remains immutable and traceable across the investigative chain. This technology helps maintain trust and transparency, as any tampering becomes detectable. Additionally, a public-facing chatbot provides real-time information on forensic standards, enhancing public understanding and trust in evidence handling practices. This dual approach strengthens security and accessibility, fostering greater accountability in forensic processes.