In this paper, we introduce a hand-gesture based control interface for navigating a car robot. A 3-axis accelerometer is used to record a user hand gesture .The data is transmitted wirelessly via an RF module to a microcontroller. The received signals are then classified to one of six control commands for navigating a car robot. The microcontroller then classifies the hand tracjectories. Simulation result show that the classifier could achieve a 92.2% success rate.

Our solar-powered beach cleaning system is transforming the way we maintain clean beaches. It is a self-sustaining setup for an automatic cleaning robot. The efficient solar panels serve as a charging point, allowing the robot to work continuously without human intervention. Even during periods of low sunlight or at night, the robot can continue its cleaning tasks thanks to its battery storage.

This paper comprises of comparative review for design of water rescue robot. The paper also gives information about techniques used for water rescue and evolution of water rescue robots in world. The comparison for design of water rescue robot is done on basis of propulsion system, power supply, microcontroller, 3D material etc. The paper also describes a calculation required for developing and designing a water rescue robot.

“Pharma-Easy” is an innovative e-commerce platform revolutionizing the pharmaceutical industry. This research facilitates the sale of a comprehensive range of medicines while introducing a unique approach to pharmaceutical sustainability. In addition to providing a diverse catalog of medications, Pharma-Easy of ers exclusive discounts on medicines nearing expiration. By alerting users to upcoming expirations and incentivizing purchases of these medications at discounted rates, the platform not only ensures af ordability for customers but also minimizes pharmaceutical waste. z’Pharma-Easyz’ also analyzes the customer request and provides proper medications available based on the prescriptions of the user. Through secure transactions, prescription verification, and prompt delivery services, Pharma-Easy aims to redefine the e-commerce landscape by combining convenience with a commitment to accessibility, af ordability, and sustainability in the pharmaceutical sector. By addressing current challenges in emergency medical services, the research recognizes the critical importance of rapid access to healthcare during medical emergencies, acknowledging that swift response can be a matter of life and death.

“Enforce360: YOLOv8 and Tesseract-OCR for Comprehensive Helmet Rule Adherence” Motorcycle riders not wearing helmets pose a significant risk to road safety and their own wellbeing. However, manually monitoring a large volume of traffic across an entire city to identify all helmet violations is an impossible task. Conversely, automatically extracting license plate information from vehicles promises various applications like seamless toll payments, parking systems, etc. but relies heavily on expensive infrastructure changes. This paper tackles these challenges by developing a real-time computer vision system to automatically detect helmet violations among motorcycle riders while concurrently extracting their license plate details. By employing advanced machine learning algorithms for object detection and optical character recognition, the system can accurately identify riders without helmets and capture license plate numbers with high precision. The real-time nature allows it to monitor live traffic streams and generate actionable insights for law enforcement and other agencies. The proposed techniques provide an efficient and low-cost automated framework to improve road safety by increasing compliance to helmet rules. Additionally, the license plate data extraction opens up possibilities for various smart city applications without needing large investments into infrastructure changes. The system has the potential to process large volumes of traffic across cities and contribute significantly to road safety and intelligent transportation initiatives.

“A Review on Enhancement of Bandwidth for RFID Applications using HFSS Software” explores the use of Advanced System Simulator (HFSS) software to improve bandwidth for radio frequency identification (RFID) applications. This article focuses on addressing the limitations of traditional RFID techniques and reviews recent advances in bandwidth enhancement using HFSS. Various technologies are being examined, including advanced materials, improved antenna design, and new electrical installations. Practical applications in the industry show the real impact of these developments. The review concludes by discussing current issues and presenting future research directions to advance RFID technology towards higher efficiency.

The conventional motorbike remains a popular transportation choice due to its cost-effectiveness. However, safety concerns persist, particularly regarding balance issues, which are crucial for rider safety. Addressing this, we propose self-balancing technology for two-wheelers. This innovation aims to provide a car-like safety level at a lower cost, enhancing accessibility to safer transportation. In addition to safety, traditional motorbikes contribute to environmental problems through fuel consumption. As the world faces climate challenges, transitioning to electric-powered vehicles becomes essential. By integrating gyroscopic technology into two-wheelers and utilizing electric power, we can mitigate safety risks and environmental impact. Gyroscopes sensors, motors, and batteries, enable automatic balance control, making two-wheelers safer and more inclusive for various riders, including handicapped individuals and children. Moreover, electric power eliminates harmful emissions, promoting cleaner transportation solutions. Thus, self-balancing two-wheelers offer a dual benefit: improved safety and reduced environmental footprint, aligning with the global push towards sustainable mobility.

: Due to the complex structure of tensegrity systems it is often hard to understand their behaviour and estimate their mechanical properties. As a result, they are rarely used in real engineering applications. The present paper focuses on the evaluation of equivalent mechanical properties of various tensegrity modules based on a con- tinuum model. The aim of the applied model is to facilitate the identification and understanding of mechanical characteristics of tensegrities through their comparison with a continuum body with equivalent features. The model is built by assuming that the strain energy of an unsupported tensegrity module or structure is equivalent to the strain energy of a solid. The approach enables to estimate the influence of self-stress on deformation of the structure, identify the influence of cables and struts on the properties of the whole system, determine equivalent mechanical characteristics, such as Young’s moduli, shear moduli, Poisson’s ratios etc., and conditions that limit their values. What is more, a qualitative as well as quantitative evaluation and comparison of mechanical properties for various tensegrity modules and systems is possible with the use of the applied technique. A comprehensive analysis of typical tensegrity modules with various elastic symmetries is presented. Using the adopted model, mechanical characteristics are determined and graphs of identified mechanical coefficients for five typical tensegrity modules are presented in relation to the self-stress multiplier and cable to strut properties ratio. The analysis can be useful for the development of extreme mechanical properties of smart tensegrity- inspired 2D or 3D lattices or metamaterials. Tensegrity Structures consist of strings (in tension) and bars (in compression). Strings are strong, light, and foldable, so Structures have the potential to be light but strong and deployable. pulleys, Nitti wire, or other actuators to selectively tighten some strings on a tensegrity structure can be used to control its shape. This article describes some principles we have found to be true in a detailed study of mathematical models of several tensegrity structures. We describe properties of these structures which hold quite generally. We describe how pretending all strings of a tense pity makes its shape robust to various loading forces. Another property asserts that the shape of a tensegrity structure can be changed substantially with little change in the potential energy of the structure. Thus shape control should be in expensive. This is in contrast to the control of classical structures which require substantial energy to change their shape.

This review paper aims to provide a comprehensive analysis of the current state of energy storage systems in India. With the increasing demand for reliable and sustainable energy, the role of energy storage has become crucial in addressing the intermittency of renewable energy sources and ensuring grid stability. The paper explores various types of energy storage technologies deployed in India, their challenges, and potential solutions. The analysis is based on a thorough review of existing literature, data, and case studies. The paper also includes tables and graphs to illustrate key findings.

The Aerial Delivery of Life-Saving Supplies is an Unmanned Aerial Vehicle (UAV) system designed to swiftly and securely deliver medical supplies during emergencies. Its primary objective is to expedite the delivery of critical supplies to hospitals, mitigating the risk of fatalities due to shortages. To prioritize environmental sustainability and efficiency, the UAV will be powered by batteries, and the use of plastic in packaging will be minimized. The incorporation of parachutes ensures the safety of both humans and supplies, reducing delivery time. The successful implementation of UAVs for medical supply delivery has demonstrated remarkable results, gaining widespread adoption worldwide.

A novel approach to harnessing wind power is introduced, demonstrating superior performance compared to conventional wind turbines of similar size and aerodynamic features in identical wind conditions. This innovative system achieves markedly increased output at a reduced cost. The primary groundbreaking aspect involves eliminating tower-mounted turbines, a characteristic feature of current wind power technology known for its mechanical complexity, towering structures, high costs, and associated hazards to both people and wildlife. The second distinctive feature of INVELOX lies in its ability to capture wind flow through an omnidirectional intake, eliminating the need for passive or active yaw control. Additionally, it accelerates the airflow within a shrouded Venturi section, subsequently releasing it into the surrounding environment through a dif user. Simulating the performance of this wind delivery system poses a considerable challenge due to the intricate nature of the system and its interaction with the incoming wind at the front end and the turbine at the rear end. The objectives of the current study include fabricating a model to gain insights into the flow field within the INVELOX, both where the actual wind turbine is located and in the external flow field. Enhanced wind velocities are shown to significantly enhance power output.

The landscape of energy management systems is undergoing a transformative shift, transitioning from centralized management to distributed energy management systems for enhanced energy ef iciency. Within the realm of distributed energy management, two distinct user types emerge: 1) those with surplus energy generation and 2) those facing a deficit in energy generation compared to their demands. This project aims to address the energy generation imbalance among distributed users through the implementation of a peer-to-peer (P2P) energy trading platform. Notably, blockchain technology is proposed as a pioneering solution, providing a secure and reliable framework for executing P2P transactions without the need for centralized broker intervention. The decentralized application characteristics inherent in blockchain facilitate the trading of energy generated by distributed users within the proposed platform. To augment user accessibility, a mobile application is established for monitoring transaction information, including transaction details and energy prices. This initiative aims to create a more balanced and efficient energy ecosystem.

This project presents a groundbreaking one-wheeled electric bike integrated with a wheelchair to facilitate seamless mobility for individuals with leg disabilities. The core innovation lies in the incorporation of a sophisticated Brushless DC (BLDC) motor and state-of-the-art attachments commonly associated with electric bikes. The resultant system of ers notable advantages, providing enhanced independence and improved maneuverability for users. However, technical evaluations reveal challenges in adapting to user dynamics and nuanced maneuvering, constituting inherent disadvantages. Furthermore, the system exhibits limitations in its responsiveness to variable terrains and environmental conditions. Despite these challenges, the project marks a significant stride in assistive mobility technology, showcasing potential transformative impact. Ongoing refinements and adaptive strategies are essential to address identified limitations, ensuring a comprehensive and technically sound solution for individuals with leg disabilities.

One of the major problem that the world faces this moment is Energy Extremity. It can be reduced to a certain extent by duly covering our energy consumption and avoiding energy destruction. Currently people face numerous problems like power theft. This system will find energy theft fluently. This IoT electricity meter is conforming of At mega 328 microcontroller with a WIFI module for IoT connection and GSM module for mobile connection, on which client will admit information via SMS. This smart electricity meter also consists of a current detector that sends the current reading to the microcontroller. We have to connect cell phones with the system via SMS which will help to configure with the system. In case of an exigency, the information will be shared on the configured number. We have to set costs for the unit and for which we have four buttons. With the help of buttons, we can set costs for the unit. As we start the system, it shows reading on the IoT screen. Reading will be changed with respect to time. In the case of energy theft, the theft will be caught and displayed on the IoT screen. Indeed the information will be entered through SMS on the configured number. After entering the alert, the driver can switch of the system using IoT to avoid thef

In this study, an IoT-based feeding and drinking system for pet dogs will be discussed. Frequent feeding and drinking causes issues for pet owners while they are away from their animals or not at home. This issue can be resolved by combining I0T technology with feeding and drinking apparatus. In this study, the I0T system developed an Internet of Things (IoT)-based system with remote control and monitoring to facilitate pet owners provision of food and water through smartphone apps. Dogs are the pets discussed in this study. There are two primary purposes for this system, eating and drinking. These features can all be used both automatically and manually. Weighing food is the annex tension function, in addition to these two functions. It is possible to have automated drinking and feeding

Introducing the Lemon Shikanji Making Machine with IoT Integration, a revolutionary appliance for effortless lemon shikanji preparation. With a user-friendly mobile app, control and monitor the juicing process remotely. This efficient machine automatically cuts, squeezes, and extracts lemon juice, eliminating manual labor. The IoT system offers insights into juicing habits, sends maintenance alerts, and suggests recipes based on preferences. Embrace cutting-edge technology for a seamless and refreshing experience in the world of healthy beverages.

The implementation of advanced safety measures in 3-phase systems is a significant step in ensuring workplace security. Utilizing a variety of sensors, including those for voltage, current, and temperature, these systems enhance overall safety. Notably, the integration of IoT technology allows real-time transmission of sensor data to a cloud-based MERN stack platform, enabling centralized data storage, analysis, and visualization. This facilitates efficient monitoring, proactive maintenance, and rapid response to potential hazards. The collected data provides valuable insights for predictive maintenance, and the user-friendly MERN stack-based interface ensures easy access to vital information, contributing to both industrial safety and operational efficiency.

The effectiveness of solar panels can be considerably reduced by the collection of dust and soil. Periodically cleaning them is essential, but it can be difficult and expensive when there is a shortage of water. The effect of dirt and dust on solar photovoltaic conversion efficiency has been the subject of recent research. Appropriate cleaning methods can increase conversion efficiency by 15-20% or output energy by around 25%. In addition to discussing automated cleaning solutions for solar photovoltaic modules, such as electrical, mechanical, chemical, and electrostatic approaches, this project gives an overview of the dust problem. The primary goal is to develop efficient automated solar panel cleaning solutions.

Cotton seed is a valuable agricultural by product, rich in oil content, and finding efficient methods for its extraction is vital in the production of cotton seed oil. This abstract provides an overview of cotton seed extraction and oil extraction machines, highlighting their significance, functionality, and technological advancements. Cotton seed oil is widely utilized in various industries, including food, cosmetics, and biofuels making the extraction process critical for maximizing its utility. The first section of this review examines the significance of cotton seed oil and its widespread applications, emphasizing the growing demand for high-quality cotton seed extraction and oil production. Subsequently, we delve into the primary methods employed for cotton seed extraction, such as mechanical pressing, solvent extraction, and aqueous extraction, each with its advantages and limitations. The heart of this review focuses on the various machines and technologies developed for cotton seed extraction and oil extraction. Their view further discusses solvent extraction processes and equipment, highlighting their efficiency and the associated challenges related to safety and environmental concerns. Technological innovations, such as automated control systems, have significantly improved the precision and efficiency of these machines.

The Shorecleanbot is a groundbreaking automated seashore cleaner robot designed to enhance coastal sustainability by leveraging Arduino technology. Its primary goal is to clean seashores and promote the well-being of marine environments, addressing the growing issue of beach pollution caused by visitors. With reports highlighting the significant accumulation of plastic waste on beaches, often inadequately managed by local authorities, this environmental damage not only threatens marine ecosystems but also diminishes the appeal of beaches for tourism. To combat this problem, the automated seashore cleaner robot operates autonomously, minimizing the need for human intervention. Equipped with versatile sensors, it ensures efficient cleaning without interruptions. The robot strategically initiates its cleaning process at night, synchronized with high and low tide schedules, covering a substantial area before sunrise to reduce potential encounters with human visitors. Utilizing data storage, it retains its cleaning path, ensuring comprehensive coverage. To handle waste collection effectively, a front loader bucket with an integrated motor allows the robot to lift and empty the garbage bucket when it reaches capacity. This innovative solution aims to contribute to cleaner and more sustainable seashores, preserve marine ecosystems, and maintain the allure of beaches for tourists.

The objective of this project is to study & understand the detailed knowledge of Beach Garbage Cleaning Machine. There are various types of Beach Garbage Cleaning Machines used for cleaning operations for coastal pollutions. These machines are used for cleaning large areas where various types of debris, such as plastics, litter, seaweed, and driftwood, from sandy and rocky shorelines where heavy and complex Beach Garbage Cleaning Machines are required. In order to overcome this problem, a small Beach Garbage Cleaning Machine is developed for cleaning the litter, seaweed, plastics and many more various types of garbage’s from diverse beach environments, including those with high foot traffic and remote, less accessible locations while minimizing disruptions to natural habitats and wildlife at the same time. These machines provide an efficient, cost-effective, and sustainable solution to beach clean-up, preserving the natures aesthetics of coastal areas and safeguarding marine ecosystems.

This research presents a Smart Agriculture Robot equipped with sensors for real-time monitoring, seed sowing automation, and crop prediction through machine learning. The integration of sensor data enhances precision in agricultural activities, while machine learning algorithms analyze historical and current data to predict crop outcomes. The system aims to optimize resource utilization and enhance overall agricultural efficiency for sustainable and informed decision-making. The smart agriculture robot described employs sensors for monitoring, seed sowing, and crop prediction through machine learning. This innovative system combines real-time data from various sensors to optimize agricultural processes, ensuring efficient seed placement. Additionally, machine learning algorithms analyze historical and current data to predict crop outcomes, enabling proactive decision-making for improved yield and resource utilization.

This research presents a Smart Agriculture Robot equipped with sensors for real-time monitoring, seed sowing automation, and crop prediction through machine learning. The integration of sensor data enhances precision in agricultural activities, while machine learning algorithms analyze historical and current data to predict crop outcomes. The system aims to optimize resource utilization and enhance overall agricultural efficiency for sustainable and informed decision-making. The smart agriculture robot described employs sensors for monitoring, seed sowing, and crop prediction through machine learning. This innovative system combines real-time data from various sensors to optimize agricultural processes, ensuring efficient seed placement. Additionally, machine learning algorithms analyze historical and current data to predict crop outcomes, enabling proactive decision-making for improved yield and resource utilization.

This paper provides an in-depth comparison of four stone cutting Techniques : Water Jet Cutting, Gang Saw (Frame Saw) Machine, Stone Edge Cutting Machine, and Multi-Wire Saw Machine. Each Techniques working principle, advantages, and limitations are thoroughly examined, highlighting their efficiency, precision, versatility, and environmental impact. By elucidating the operational intricacies and performance metrics of each Technique, this study aims to assist industry professionals and researchers in making informed decisions regarding stone cutting techniques for various applications.

The objective of this project is to presents an innovative approach to manual cutting tools, introducing a human-powered solution that leverages the time-tested crank-slider mechanism. This project integrates the concept of the crank-slider mechanism, a classic mechanical engineering principle. This mechanism seamlessly converts rotary motion into reciprocating motion, making it an ideal choice for a human-powered pedal-operated hacksaw. This innovation capitalizes on the robustness and endurance of the muscles of human leg, which are inherently better suited for sustained physical effort compared to arm muscles. The applications of the paddle-operated hacksaw extend across a wide spectrum of materials, including metals and plastics. Its versatility is particularly valuable in industries such as metalworking and furniture production. Beyond productivity, the machine has the unique ability to double as an exercise tool, promoting physical fitness while engaging in practical tasks. This dual-purpose feature aligns with the growing emphasis on integrating physical activity into daily routines. Precise construction and well-calibrated components ensure that the pedal-operated hacksaw delivers efficient and reliable cutting capabilities. The seamless integration of the crank-slider mechanism optimizes the conversion of human effort into mechanical work, enhancing overall cutting efficiency and minimizing physical strain on the operator. Moreover, its potential application in various industries can help reduce peak electricity demand and grid fluctuations in regions heavily reliant on electrical power for similar cutting tasks, since it does not need any electricity to operate functionally.

We the group of engineers designing and developing domestic green house. Green house farming and hydroponic farming are two innovative approaches to modern agriculture that address the challenges of food production in changing world. Greenhouse provides a controlled environment for plants, allowing better regulation of temperature, humidity, and light. It provides environmental factors, leading to efficient nutrient absorption and faster growth. Hydroponics, a modern farming technique, is gaining popularity for its space-efficient, resource-minimizing approach. This paper reviews its history, types, and principles, highlighting advantages like high yields, cultivation in poor soil, and reduced water/pesticide use. Limitations, including initial costs, technical expertise, and reliance on consistent resources, are also discussed. Emphasizing careful evaluation, the paper concludes by urging further research to optimize hydroponics for large-scale implementation and address challenges faced by farmers. In India, traditional farming methods lead to challenges like unpredictable crop outcomes. Our proposed system leverages advanced technology, offering automatic monitoring of factors like water level, temperature, humidity, and light. By automating control measures and providing a comprehensive crop schedule, farmers can optimize resource usage, mitigate risks, and improve overall crop production, eliminating the need for time-consuming on-site surveys. This system addressing the limitations posed by traditional methods and promoting efficient, data-driven agriculture. This research is emphasis on development of an equipment to help the metropolitan through economical point of view. Our research predicts further exploration based on the rough diagram and solid model of project.

This research introduces an innovative table tennis machine designed to revolutionize training for players of all levels. The compact device features an advanced robotic arm with versatile shot replication, from topspin to backspin. Userfriendly customization, accessible through a mobile app or touchscreen, tailors settings to skill levels. Despite lacking an AI system, the sophisticated control system ensures precision in shot placement, speed, and frequency. A notable feature is the efficient ball-recycling system, reducing the need for manual reloading and enhancing practice efficiency. Rigorous testing, in collaboration with professional players and coaches, guarantees the machines reliability. With its dynamic training features, this mechanical table tennis machine is set to transform the sport, providing a valuable tool for players and coaches alike, and contributing to the evolution of table tennis training standards.

Based on the identified problem by our group of the unavailability of affordable commercial usable battery pack for electric vehicles and with the goal of implementing water cooling for the same which will lead to these packs be more compact and efficient we have decided to undertake this project . Research regarding calculating the cell configuration according to the energy requirement as well as suitable selection of the motors done. Later once the cell configuration was decided and the battery pack was designed, thermal simulations were carried out to validate whether water cooling was more efficient than air cooling for the pack. It was found the water cooling provides more reliable and consistent cooling as compared to air cooling, but it also allows us to design a more compact cell module thus making the design the entire pack more efficient.

Machinery fault diagnosis is vital in maintaining industrial machinery, focusing on identifying issues through various tools to prevent breakdowns and ensure efficient operation. In machine learning, accurately detecting and predicting faults poses challenges such as data quality, imbalanced datasets, and interpretability. Addressing issues like feature selection, generalization, early fault detection, data labeling, and real time processing is crucial for predictive maintenance. Advanced techniques like machine learning and data analytics analyze data, identify patterns, and predict potential failures. However, ongoing challenge persist in interpreting results and implementing effective maintenance strategies, emphasizing the complexity of machine fault diagnosis in industrial settings.

This abstract details a cold forging industry process improvement initiative, addressing stock out prevention, inventory management, and audit scores. Cold forging companies often struggle with disruptions due to stock outs and inefficiencies. A project report, utilizing market surveys and data acquisition, identified areas for enhancement. Advanced inventory tools optimized stock levels and supply chain processes, reducing stock outs and improving efficiency. Implementing these findings streamlined operations, cut inventory costs, and boosted the industrys audit score significantly. This case study illustrates the tangible benefits of data-driven approaches and process optimization, enhancing operational efficiency and customer satisfaction for manufacturers.

Establishment of efficient machining parameters has been a problem that has confronted manufacturing industries for nearly a century, and is still the subject of many studies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Determination of the optimal cutting parameters (cutting conditions) like the number of passes, depth of cut for each pass, speed, and feed is considered as a crucial stage of multi-pass machining as in the case of all chip removal processes and especially in milling operation. The effective optimization of these parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This project outlines the development of an optimization strategy to determine the optimum cutting parameters for milling operations like plain milling and face milling. This project also underlies the importance of using optimization strategies rather than handbook recommendations as well as pointing out the superiority of the multi-pass over the single-pass optimization approach. The project discusses both single-tool and multitool milling operations where emphasis has been placed on the latter. Although many efforts have been made to optimize machining parameters, from the review of the published literature it can be concluded that most of the work done is restricted to turning operations, and other machining operations, including milling, have gained little attention. Owing to the significant role that milling operations play in todays manufacturing world, there is a vital need to optimize machining parameters for this operation, particularly when NC machines are employed.

This study explores how FDM process parameters and filament type affect the compression strength of FDM-printed dental parts. The study finds that infill density, layer thickness, nozzle temperature, and filament type have significant impacts on compression strength, while print speed has a negligible impact. The study provides guidance for selecting the optimal printing settings and materials to produce high-quality dental parts.

The Universal 3D Printer Manufacturing Machine project is a groundbreaking initiative aimed at revolutionizing the 3D printing industry by creating a versatile and adaptable 3D printer that can address the limitations of existing technology. In an era where 3D printing has become integral to various industries, the need for a single, all-encompassing 3D printer that can cater to a wide range of materials, applications, and industries is evident. The projects core objectives include designing a 3D printer capable of working with diverse materials, developing user-friendly software for customization, implementing innovative technology like multi-axis printing and automated material handling, and conducting an environmental and cost-effectiveness assessment in comparison to traditional manufacturing methods. This multidisciplinary approach brings together experts from mechanical engineering, materials science, software development, and environmental studies to create a flexible and robust 3D printer prototype, and user-friendly software, and perform extensive testing for material compatibility and application suitability. Ultimately, the project seeks to achieve the creation of a Universal 3D Printer Manufacturing Machine, empowering users with a powerful, customizable, and sustainable 3D printing solution while also assessing its environmental and economic impact. The significance of this project lies in its potential to disrupt traditional manufacturing methods, offering a more sustainable and cost-effective approach, benefiting a wide array of industries, and fostering economic and environmental sustainability. By disseminating knowledge and findings, this project aims to drive innovation, collaboration, and the adoption of a more versatile and eco-friendly approach to additive manufacturing, fundamentally changing the way objects are produced and customized across various sectors.

Efficiency and productivity are critical in the competitive industrial landscape. Time and Motion studies are recognized as patent tools for process optimization. This abstract proposes a Comprehensive industrial project: a detailed time and motion study to boost efficiency in a Manufacturing facility. The project’s primary aim is to identify and eliminate inefficiencies, trim Production costs, and enhance overall productivity. The methodology involves systematic Observations, data collection, and task analysis. Scrutinizing task motions and times aims to Pinpoint improvement opportunities. The project’s significance lies in its ability to provide data-driven recommendations. It is anticipated that study outcomes will enable informed Decisions, including process reengineering, workflow adjustments, and equipment Upgrades. The project includes phases such as data collection, analysis, recommendations, and Implementation. Collaboration with key stakeholders, including factory workers, supervisors, and management, is essential for success and practicality. In conclusion, this industrial project Offers a unique opportunity to enhance manufacturing efficiency. Leveraging insights gained, the company can remain competitive, reduce waste, and improve its bottom line.

A groundbreaking effort is ongoing to design, develop, and implement a solar-powered electric vehicle (EV) for inter-campus transportation, with the goal of satisfying the mobility demands of students, teachers, and staff while lowering carbon emissions. This unique approach exemplifies sustainability and renewable energy goals by using solar panels to power the EV and considerably reducing the environmental effect of driving between two campuses of colleges. This project, which promotes electric mobility and renewable energy, demonstrates a forward-thinking approach to transportation and encourages an innovative and sustainable culture within the academic community, harmonizing with the institutions commitment to environmental responsibility.

Automation is the application of various technologies based on robotics or computer software to control machines and processes by independent systems. In order to handle the necessary levels of quality control, production labour shortages, and overall profitability, automation is now required in the food sector. In places where humans were once common, automation has taken over. Industry leaders are aware that in order to satisfy customer needs and requests, plant systems must continuously adapt. This entails buying new equipment or updating current equipment. Due to fierce competition from retailers who are always lowering prices, growing expenses for raw materials, and skyrocketing utility bills in a labour-intensive production setting, food producers are considering automating their manufacturing operations. Food manufacturing automation has a lot of advantages. The capacity to duplicate the look and feel of Automation is the application of various technologies based on robotics or computer software to control machines and processes by independent systems. In order to handle the necessary levels of quality control, production labour shortages, and overall profitability, automation is now required in the food sector. In places where humans were once common, automation has taken over. Industry leaders are aware that in order to satisfy customer needs and requests, plant systems must continuously adapt. This entails buying new equipment or updating current equipment. Due to fierce competition from retailers who are always lowering prices, growing expenses for raw materials, and skyrocketing utility bills in a labour-intensive production setting, food producers are considering automating their manufacturing operations. Food manufacturing automation has a lot of advantages. The capacity to duplicate the look and feel of a product with the minimum use of ingredients not only improves line efficienciesleading to bottom-line Profit, but also can potentially increase sales. At the same time,improving the traceability of raw ingredients will provide the Added benefit of improved foodsafety.

This paper introduces a novel solution to the incíeasing demand foí veísatile and space-efficient fitness equipment: the Multipuípose Fitness Machine (MFM). With physical fitness being a coíneístone of a healthy lifestyle, theíe is a cíitical need foí innovative appíoaches that addíess common baííieís to íegulaí exeícise such as time constíaints, lack of gym access, and limited space at home. traditional fitness equipment often falls shoít in these íegaíds, being bulky, expensive, and specialized. lhe MFM aims to íevolutionize home fitness by offeíing a single appaíatus that integíates vaíious exeícise modalities into a compact and useí-fíiendly design. By píioíitizing sustainability thíough the use of íecycled mateíials and eneígyefficient constíuction, the MFM not only píovides a solution to the space constíaint issue but also aligns with global effoíts towaíds íesouíce efficiency. lhis abstíact outlines the significance of the MFM in píomoting accessible and effective exeícise options foí individuals of all fitness levels and preferences.

This research paper presents a systematic approach for the maintenance of heat pump test rigs. The approach includes steps, tools, and techniques used for maintenance, scheduling regular maintenance, and highlighting the benefits of a properly maintained heat pump test rig. The paper emphasizes the importance of maintaining heat pump test rigs and discusses the common issues faced during maintenance. The presented approach can help organizations reduce downtime, increasing the lifespan of heat pump test rigs, and improving the overall efficiency of the system. The paper contributes to the field of heat pump test rig maintenance by presenting a structured approach that can be adopted for the maintenance of heat pump test rigs.

This review presents a convincing analysis of the Personal AI Gym Trainer, showcasing it as a ground-breaking development in tailored training and fitness. Priority is placed on the application of cutting-edge artificial intelligence technologies, showcasing the systems capacity to provide a customized and thorough workout experience. Personalized workout plans, nutritional guidance, and progress tracking are made possible by the multi-modal approach, which combines voice commands, facial recognition, and instantaneous data processing. The trainers capacity to adjust to each persons preferences, physical capabilities, and goals is clearly communicated in the paragraph, which highlights the trainers dedication to providing highly individualized training programs that maximize ef iciency and minimize risk of injury. Reviewing the technologys transformational potential overall, it highlights its salient characteristics, advantages, and potential applications, and suggesting a revolutionary impact on individuals engagement with fitness routines and overall well-being.

Our research paper focuses on addressing the issue of accidents caused by speed and aims to save lives by implementing an automatic accident detection system. When an accident occurs, the system utilizes various components to identify it and immediately notifies the emergency services. The key feature is the systems ability to accurately determine the location of the accident using latitude and longitude coordinates, which are then sent to the nearest emergency service provider. The primary objective is to rapidly identify accidents and notify rescue teams promptly to ensure timely assistance. This research introduces a dedicated mobile application with the primary goal of improving emergency healthcare. The research aims to streamline ambulance dispatch and collaborate with traf ic authorities to ensure smooth passage for ambulances. By addressing current challenges in emergency medical services, it recognizes the critical importance of rapid access to healthcare during medical emergencies, acknowledging that swift response can be a matter of life and death.

The construction industry stands as a dynamic force, sculpting the very fabric of societies across the globe. Yet, amidst this dynamism, traditional project management methods have proven inadequate in the face of the evolving demands of modern construction endeavors. It is within this context that a groundbreaking construction application emerges, meticulously designed to surmount these challenges. This revolutionary app represents a paradigm shift in how construction projects are planned, executed, and monitored. By seamlessly integrating cutting-edge technology with tried-and- true construction principles, it promises to streamline operations and enhance productivity across the board. Its intuitive interface empowers project managers, architects, and workers alike, providing them with real-time access to critical data, schedules, and resource allocations. Furthermore, this app embraces the collaborative nature of construction projects, fostering open communication and cooperation among stakeholders. Through features such as generating 2D-model of infrastructure, predicting height of building by observing the natural calamities. By harmonizing these capabilities, it pledges to greatly amplify ef iciency, reduce delays, and maximize resource ef iciency. This exploration delves into the apps inception, highlighting its pivotal features and the transformative influence it wields in construction project management, ushering in an epoch of heightened productivity and ef ectiveness which also helps for empowering labors and selling/buying lands.

Potholes are a significant inconvenience for drivers and can also result in substantial damage to vehicles. RoadEye is a pothole detection system that uses dashcam footage to identify and report potholes to road maintenance authorities. This system can help to make roads safer and more ef icient, and can also save drivers money on repairs. RoadEye uses a deep learning model to identify potholes in dashcam footage. The model has been trained on a large dataset of images of potholes. When the model is presented with a new image, it can identify whether or not it contains a pothole, and if so, it can easily add its location on a map, so that users can be alerted while approaching a pothole. RoadEye can help to improve data collection on road conditions. This data can be used to make informed decisions about road maintenance and infrastructure planning. RoadEye can be used to create a crowdsourced database of potholes. This database can be used by drivers to plan their routes and avoid potholes.

In an era where the need for enhanced security is paramount, the Secure Bioguard-Next Generation Security System represents a significant evolution in access control and authentication methods. As the world becomes increasingly interconnected, safeguarding sensitive information, physical spaces, and digital assets has never been more critical. Biometric authentication systems have emerged as a ground breaking solution to address these security challenges. Among these, the integration of multiple fingerprint recognition technologies stands out as a cutting-edge approach to enhancing access control and authentication. This abstract delves into the world of multi-fingerprint security systems, exploring their development, advantages, applications, and the implications they hold for the future of secure access control. The concept of "Secure Bioguard-Next Generation Security System multi-fingerprint security systems represents a significant leap forward in addressing modern security challenges. By combining the strengths of multiple fingerprint recognition techniques, such as capacitive, optical, and ultrasonic, these systems offer a more robust and versatile approach to authentication.

Brain tumor detection is a crucial task in the realm of medical diagnostics, bearing significant implications for patient care and outcomes. This research paper embarks on a comprehensive exploration of the development and deployment of an advanced brain tumor detection system. The methodological framework is multifaceted, commencing with the assembly of a diverse and extensive dataset of brain imaging scans. Subsequently, the data undergoes rigorous preprocessing, including noise reduction and image enhancement, to optimize the quality and fidelity of the scans. The heart of the system lies in the utilization of deep learning, particularly a convolutional neural network (CNN), which leverages the robust features extracted from the preprocessed data to distinguish between brain scans indicative of tumors and those that are not. Model training is augmented by the introduction of a validation set, allowing for finetuning to achieve optimal performance. Testing the trained model on an entirely separate and previously unseen dataset substantiates its real-world utility, providing critical insights into its robustness and accuracy. The practical implementation of the system involves seamless integration into a real-time processing platform, enabling rapid analysis of incoming brain imaging data. This operational phase includes the establishment of predefined thresholds, effectively reducing false alarms and ensuring that only the most probable cases are flagged for review by medical professionals.

In the dynamic landscape of contemporary healthcare, efficient resource management is paramount, particularly in bustling hospital environments. This surveillance system introduces an innovative solution to address challenges in hospital crowd management. Leveraging advanced surveillance technology and real-time data processing, the system enhances patient experience by notifying individuals about unoccupied seats in waiting areas, optimizing resource utilization and minimizing inconvenience. Prioritizing patient well-being, the system contributes to judicious healthcare resource allocation. Providing healthcare personnel with real-time insights into seat occupancy, the system enables proactive responses, ensuring organized and streamlined patient flow. It represents a pioneering approach to hospital crowd management, featuring real-time oversight and communication capabilities. Through sophisticated image processing and machine learning, the system continually monitors waiting area occupancy, promptly notifying waiting patients of vacant seats, reducing wait times, and enhancing overall satisfaction. Dynamic resource allocation ensures patients are directed eficiently, benefiting both patients and healthcare facilities. This healthcare resource management system, with its comprehensive features, addresses immediate crowd management challenges and establishes itself as a holistic solution for elevating patient experiences and overall healthcare operational efectiveness.

Fantasy sports have gained immense popularity in recent years, and platforms like Dream11 and Vision11 have become the go-to destinations for sports enthusiasts looking to test their sports prediction skills. The success of a fantasy team hinges on the selection of the right players, a task that requires a deep understanding of various factors influencing a game. PredictXI is an innovative machine learning-based system designed to simplify and enhance the process of creating winning fantasy teams for prediction apps like Dream11 and Vision11. PredictXI harnesses the power of advanced machine learning algorithms to analyze a multitude of critical variables that impact a players performance and a teams success The proposed system also considers multiple factors to make informed decisions on player selection & team composition for a particular match. By processing these variables and employing sophisticated predictive modeling techniques, PredictXI narrows down the best players to include in your fantasy team. PredictXI stands as a cutting-edge solution that empowers users with data-driven insights, increases the likelihood of assembling a winning fantasy team, and enhances the overall fantasy sports experience.

InveStar is a groundbreaking platform that offers a fresh perspective on content investment, connecting fans with their favorite influencers in a unique and mutually beneficial way. At its core, InveStar allows users to invest in digital collectible cards that feature their beloved creators. These cards are not just aesthetic items; they represent a direct stake in the creators income, meaning that investors can partake in the financial success of the material that these influencers have created. This innovative approach not only cultivates a sense of involvement but also allows fans to potentially earn returns on their investments. The exclusive cards offered on the platform serve as not only tokens of fandom but also as potential digital assets that can accrue in value over time, contingent on the creators popularity and prosperity. One of InveStars key strengths is its use of blockchain technology. By leveraging the security and transparency of blockchain, it ensures the safety of investments and offers a clear and immutable record of transactions and earnings. InveStar presents a compelling vision of how passion for creators can be aligned with financial opportunity in the digital age.

The block-chain-based NFT ticketing system is a revolutionary solution for the event ticketing industry, offering unprecedented security and transparency. By leveraging block-chain technology and Non-Fungible Tokens (NFTs), it addresses long-standing issues like ticket fraud and scalping. Each event ticket is represented as a unique NFT on the block-chain, making ownership and transfer completely transparent and tamper-proof. This innovative approach simplifies the ticketing process for users while preventing unauthorized resale, ensuring that only legitimate ticket holders gain access to events. This system not only enhances the security of ticketing but also improves the overall user experience. Attendees can securely purchase tickets using crypto-currencies, easily transfer tickets to friends or family, and even request refunds within predefined constraints. Furthermore, feedback and ratings for events are recorded on the block-chain, fostering trust between event organizers and attendees. The block-chain-based NFT ticketing system is ushering in a new era of trust, integrity, and convenience in the world of event ticketing.

LEGALBOT represents a groundbreaking development in the field of legal technology, aiming to democratize access to legal expertise and guidance. This innovative chatbot leverages advanced natural language processing (NLP) algorithms and a vast knowledge base to provide users with accurate legal information, advice and support. By enabling individuals to seek legal counsel and answers to their legal queries conveniently and affordably. This chatbot addresses the longstanding issues of legal accessibility and affordability. This chatbot provides an overview of the key features, benefits and potential impact of the Law Advisor Chatbot emphasizing its role in empowering individuals to make informed legal decisions and navigate the complexities of the legal system. Moreover, it highlights the chatbots potential to serve as a valuable tool for legal professionals, improving efficiency in legal research and consultation processes. Overall, the Law Advisor Chatbot presents a significant step towards a more inclusive and equitable legal landscape.

Airflow Canvas – Digital Art with Hand Gesture is an application that introduces real-time drawing functionalities through the detection of a green object within the cameras live feed. Leveraging the robust foundation of OpenCV, this application excels in tracking the movements of the green object in the surrounding air, translating these movements into dynamic drawing actions directly on the screen. The application uses Convolutional Neural Network (CNN) model, finely tuned to accurately recognize and classify the shapes generated by the user during the drawing process. This intelligent integration of computer vision techniques and advanced deep learning algorithms empowers users with an unparalleled, intuitive, and interactive drawing experience. In Airflow Canvas – Digital Art with Hand Gesture essence, represents an exceptional tool for fostering creative exploration and expression. The deployment phase integrates the trained model into the air flow canvas system, emphasizing compatibility and scalability. Post-deployment, a robust monitoring system is implemented for ongoing performance tracking and maintenance. The development process includes creating a user-friendly interface, thorough documentation, and an iterative feedback loop to continually improve the models adaptation to changing user requirements and dynamic air flow patterns. The methodology ensures a holistic and structured approach, addressing key elements from data preprocessing to user interface design.

The study includes the preparation of waste plastic used in flexible road pavement. The last decade due to ever-growing environmental concerns, the use of waste plastic has gained momentum and acceptance. Plastic provides the advantage of being economical and waterproof at the same time. Plastic waste is one such resource, a major component of solid waste that is abundantly available and disposed of without proper treatment. There has been an exponential growth in municipal plastic waste disposal especially in urban areas which deteriorates the beauty of the landscape. Plastic was found to be an effective binder for bitumen mixes used in flexible pavements. This efficient method helps the pavements to resist higher temperatures by minimizing the formation of cracks and reducing rainwater infiltration which otherwise leads to the development of potholes. These pavements have shown improved crushing and Abrasion values and Reduced water seepage. Plastic roads would be a boon for India’s hot and extremely humid climate, where temperatures frequently cross 50°C and torrential rains create havoc, leaving most of the roads with big potholes.

“Food trash production rises annually along with the growth in global population. Approximately 1.2 billion tons of waste is produced annually, of which 60 to 70 percent is agricultural waste that is disposed of in landfills. High levels of organic materials in culinary refuse can be used to create renewable energy sources. One of the important energy sources is biogas, which typically includes environmentally hazardous substances like methane, carbon dioxide, and hydrogen sulfide. Biogas can be effectively produced using anaerobic decomposition. Anaerobic digestion is carried out in the lack of oxygen with the assistance of anaerobic microorganisms. This study examined the production of biogas from kitchen refuse on the grounds of the VIVA INSTITUTE OF TECHNOLOGY. Also by designing a small sewage treatment plant for our institute it will help with the water requirement as well by making use of the waste water and utilizing it for sanitary purposes. The aim of our project is to minimize the waste that is generated from our institut

Due to the complex structure of tensegrity systems it is often hard to understand their behaviour and estimate their mechanical properties. As a result, they are rarely used in real engineering applications. The present pa-per focuses on the evaluation of equivalent mechanical properties of various tensegrity modules based on a con- tinuum model. The aim of the applied model is to facilitate the identification and understanding of me-chanical characteristics of tensegrities through their comparison with a continuum body with equivalent fea-tures. The model is built by assuming that the strain energy of an unsupported tensegrity module or structure is equivalent to the strain energy of a solid. The approach enables to estimate the influence of self-stress on de-formation of the structure, identify the influence of cables and struts on the properties of the whole system, determine equivalent mechanical characteristics, such as Young’s moduli, shear moduli, Poisson’s ratios etc., and conditions that limit their values. What is more, a qualitative as well as quantitative evaluation and com-parison of mechanical properties for various tensegrity modules and systems is possible with the use of the applied technique. A comprehensive analysis of typical tensegrity modules with various elastic symmetries is presented. Using the adopted model, mechanical characteristics are determined and graphs of identified me-chanical coefficients for five typical tensegrity modules are presented in relation to the self-stress multiplier and cable to strut properties ratio. The analysis can be useful for the development of extreme mechanical prop-erties of smart tensegrity- inspired 2D or 3D lattices or metamaterials. Tensegrity Structures consist of strings (in tension) and bars (in compression). Strings are strong, light, and foldable, so Structures have the potential to be light but strong and deployable. pulleys, Nitti wire, or other actuators to selectively tighten some strings on a tensegrity structure can be used to control its shape. This article describes some principles we have found to be true in a detailed study of mathematical models of several tensegrity structures. We describe properties of these structures which hold quite generally. We describe how pretending all strings of a tense pity makes its shape robust to various loading forces. Another property asserts that the shape of a tensegrity structure can be changed substantially with little change in the potential energy of the structure. Thus shape control should be in expensive. This is in contrast to the control of classical structures which require substantial energy to change their shape.

This study explores an innovative approach to produce fiber-cement panels for prefabricated building panels by incorporating waste rice husks and old newspapers. This eco-friendly method not only addresses waste management issues but also provides a cost-effective solution to the construction industry. With growing environmental concerns, natural fibers have garnered significant attention in recent years. This research focuses on utilizing rice husks, an agricultural byproduct, as a natural fiber for bio composites, offering an economical and sustainable alternative to conventional engineering materials. Additionally, the study examines the suitability of rice husks as fibers with various polymers, along with different modification techniques to enhance their mechanical properties. The research provides insights into the manufacturing process of rice husk-based bio composites, offering valuable information on potential future applications. The findings suggest that incorporating rice husks and old newspapers into natural fiber cement boards for building partitions is a feasible option. By developing products based on natural fiber-reinforced composites, traditional engineering materials can be replaced with alternatives that offer low cost, low density, competitive specific mechanical properties, and sustainability, making them suitable for the construction industry. However, numerous challenges remain, necessitating further research and development in this field.

Waste generation is increasing day by day with the growth of population which directly affects the environment and economy. Consequently, there are millions of tons of solid waste being produced every year which have to be safely disposed without any negative impact to the environment. On the other hand, as one of the driving forces for economic and social development the availability of energy in sufficient and sustainable amount has been becoming world’s main interest. However, depending on the way the energy is produced, distributed and used, it may contribute to environmental problems such as water, land and air pollution or even global climatic change. Waste from the Municipal Solid Waste (MSW) contributes towards maximum waste generation in India. Thus, management of waste is very much essential with the increasing demand for energy. This paper mainly focuses on reviewing solid waste management potentials, its technologies, and the associated challenges. Solid waste can be easily managed by forming the layers of landfill and the hazardous effects can be reduced by utilizing the gases emitted through this method.

This paper offers a review on the theoretical and practical use of qgis .Many individuals are deterred from utilizing geographic information system (GIS) software due to its high cost, which can hinder their ability to create maps and analyze spatial data. However, there is a free GIS software option available called Quantum GIS (QGIS) that can provide similar functionality to more expensive proprietary software. This fact sheet aims to introduce readers to QGIS and demonstrate its versatility by guiding them through basic GIS processes, such as creating land-cover maps and heatmaps, for various fields including natural resources, agriculture, and urban planning. This document is suitable for individuals who are interested in incorporating GIS into their work but cannot afford expensive software, as well as those looking to learn a new GIS software package.

Greywater recycling is a practise that is becoming more and more popular for water conservation and minimising the environmental effect of homes and buildings. Greywater can be treated and repurposed for nonpotable uses including irrigation, toilet flushing, and laundry. Greywater is the wastewater produced by sources like sinks, showers, and washing machines. The significance of recycling greywater, its advantages, and the numerous greywater treatment methods are the main topics of the abstract. The abstract also discusses some of the potential drawbacks of recycling greywater, like upkeep costs and safety issues. Greywater recycling ultimately has the ability to drastically cut water usage, lessen the effects of droughts, and improve sustainable water management techniques.

The Employee Team Recommendation System and Multiple Resume Analyzer are innovative tools designed to optimize team dynamics and streamline recruitment processes. The Employee Team Recommendation System uses advanced algorithms and machine learning techniques to analyse employee skill sets, strengths, and preferences, offering intelligent recommendations for team compositions. This system improves project outcomes and job satisfaction. The Multiple Resume Analyzer simplifies candidate evaluation by extracting, categorizing, and evaluating information from numerous resumes. This technology accelerates the hiring process, enabling organizations to make well-informed decisions. These tools offer a transformative advantage in a competitive job market, promoting seamless collaboration and data-driven recruitment practices. They transcend traditional methods, paving the way for a more agile workforce, addressing challenges like remote work, skill requirements, and increased competition. The projects impact extends beyond HR departments, influencing strategic decision-making and strengthening organizational effectiveness.

Expersion Xbot is a groundbreaking mobile application that is poised to revolutionize the way individuals understand, manage, and take control of their health. This innovative and intelligent app harnesses the power of advanced AI and natural language processing to provide users with swift, precise, and personalized insights into their health concerns. The exploration of Ayurvedic practices and personalized healthcare in the literature encompasses various dimensions, including the application of machine learning models for Prakruti identification [1]. Studies delve into the correlation between Prakruti and Sharirika Bala [2], employing intelligent Prakruti assessment with machine learning classifiers [3], and conducting clinical observational studies on Prakruti [4]. Ayurgenomics is utilized for drug discovery [5], while ensemble machine learning methods contribute to dosha prediction [6]. Surveys on human Prakruti and Tridosha [7] provide valuable insights, and a portable pulse detector is developed using artificial neural networks [8]. Recent advances underscore the fusion of genomics and Ayurveda [9], highlighting technology transformative potential. The computerized pragmatic assessment of Prakruti Dosha using tongue images demonstrates the integration of modern tools into Ayurveda for personalized treatments [11]. Clinically adaptable Prakruti assessment tools are developed, emphasizing India rich medical heritage [13] and the crucial role of Prakriti assessment in tailoring personalized Ayurvedic treatments [14]. The convergence of Ayurveda and traditional systems opens avenues for herbal drug discovery [15]. In summary, these studies collectively illustrate the synergy between technology and Ayurvedic wisdom, offering innovative approaches to holistic healthcare

The pharmaceutical industry plays a vital role in safeguarding global health. However, its supply chain is complex, susceptible to fraud, and often inefficient. Pharmaceutical supply chains are characterized by multiple stakeholders, including manufacturers, distributors, suppliers, and healthcare providers. The intricate nature of this supply chain makes it vulnerable to issues like counterfeit drugs and data inaccuracies. Blockchain, with its inherent features of transparency, immutability, and decentralization, offers a compelling framework to mitigate these challenges. Blockchain enhances transparency by creating an immutable ledger of all transactions. Each drug batch is assigned a unique identifier, recorded on the blockchain and associated with relevant data such as manufacturing details, shipping records and quality test results. Stakeholders across the supply chain can access this information in real-time, ensuring complete visibility and traceability. Counterfeit drugs cause serious problem to public health. Blockchain secure, tamper-proof records enable consumers and healthcare professionals to verify the authenticity of pharmaceutical products. Smart contracts, self-executing agreements triggered by predefined conditions can automate verification processes, reducing the risk of counterfeit drugs entering the supply chain. Blockchain cryptographic techniques ensure data security, privacy and protecting sensitive information. Pharmaceutical recalls can be costly and time-consuming. Blockchain traceability allows for targeted recalls, pinpointing affected batches quickly. Smart contracts can automate the recall process, notifying relevant parties and facilitating the return of products. This efficiency not only saves resources but also enhances patient safety.

The Multispectral Satellite Image Dehazing project endeavors to enhance the utility and quality of multispectral satellite imagery by developing specialized dehazing techniques tailored to the unique spectral characteristics of such data. Focusing on mitigating atmospheric interference, particularly haze, the project aims to improve data clarity, benefitting applications like environmental monitoring, disaster management, and land use analysis. The project core objective is to address the challenges posed by haze in multispectral imagery, offering a significant contribution to remote sensing by advancing techniques specific to the nuances of multispectral satellite data. The outcomes hold the potential to elevate the accuracy and reliability of satellite-based information, impacting various fields such as agriculture, forestry, urban planning, and climate studies.

The " IVigilance -Intravenous Drip Monitoring and Controlling System for Hospital Using IoT" project presents an IoT-driven solution designed to modernize and enhance intravenous therapy management within healthcare organizations. Intravenous (IV) drips play a pivotal role in patient care, yet their manual oversight poses potential risks, including medication errors and inefficiencies. This project leverages IoT technology to create an automated system that continuously monitors and regulates IV drip operations. By incorporating IoT sensors and microcontrollers into the IV drip infrastructure, real-time data on flow rates and drip status is collected. This data not only ensures precise medication and fluid delivery but also enables remote control and adjustment by healthcare professionals. The system benefits are twofold. First, it reduces the likelihood of human error, significantly improving patient safety. Second, it provides data-driven insights that empower healthcare institutions to optimize resource allocation and make informed clinical decisions. In summary, the "Intravenous Drip Monitoring and Controlling System for Hospital Using IoT" project offers a transformative approach to IV therapy management. It promises to enhance patient care, streamline hospital operations, and usher in a new era of healthcare technology, where IoT-driven automation ensures the accuracy and safety of intravenous therapies, benefiting both patients and healthcare providers.

In an era of fast technological innovation, the seamless integration of new technologies into old infrastructure is crucial. This effort attempts to improve data access, optimization, and organization by combining traditional technology with cutting-edge concepts. The startup, which focuses on big data analytics and edge computing, hopes to pave the path for a brighter future [1]. Today, security is critical, especially with the difficulty of abandoned homes owing to demanding duties. To address this worry, CCTV cameras are widely used to protect residences while the owners are away [8]. Furthermore, in smart cities, surveillance cameras play an important role in gathering evidence for crime prevention and investigation [3]. This project presents a new way to surveillance that combines classic video monitoring with superior motion-based sensing technology. Unlike standard systems that just take footage, the suggested system uses sensor cameras to identify and analyze the motion of visible objects. These camera reliable monitoring function is meant to spot approaching objects, allowing for a proactive response to potential security concerns [6]. The system ability to use motion detection to capture just moving frames is a crucial breakthrough, as it optimizes the use of system resources [7]. By updating the backdrop frame with prior background intensity inference, the system assesses its ability to identify and respond to physical motions appropriately [4]. This research and development work not only contributes to the growth of security systems, but it also demonstrates the successful blending of traditional and cutting-edge technology to handle current difficulties [12]. The suggested motion-based surveillance system is a viable alternative for effective and resourceful security monitoring in both residential and urban settings.

The "Smart Eyes on the Horizon: A Survey of Real-Time CCTV Innovations", revolutionizing law enforcement, is a pivotal tool that embodies cutting-edge innovation through its advanced image deblurring technology. This state-of-the-art solution plays a central role in elevating the clarity of surveillance footage, thereby not only significantly enhancing facial recognition accuracy but also streamlining the matching of images with extensive databases. Operating seamlessly in real-time, Smart Eyes on the Horizon serves as a cornerstone in proactive crime prevention, empowering law enforcement with the ability to anticipate and swiftly respond to emerging threats, ensuring they remain one step ahead in the ever-evolving landscape of criminal activities. The system intuitive user-friendly interface, combined with its scalability and customizable features, fosters widespread adoption among law enforcement agencies. Beyond addressing the critical need for improved image quality, It excels in accurate suspect identification, especially in the intricate scenarios of violence detection. This multifaceted tool is not merely a technological asset; it is a strategic ally that reinforces the capabilities of law enforcement in safeguarding communities. Moreover, Smart Eyes on the Horizon signifies a quantum leap in law enforcement technology, dynamically responding to the escalating demands for enhanced capabilities. Its commitment to prioritizing privacy and maintaining cost-effectiveness underscores its holistic approach to addressing the ethical considerations inherent in modern surveillance systems. By providing officers with unparalleled image deblurring capabilities, It equips them to respond more effectively to incidents, contributing significantly to the overarching goal of creating safer communities. In essence, Smart Eyes on the Horizon transcends being just a tool; it symbolizes the continuous evolution of law enforcement technology, ushering in an era where precision and efficiency are seamlessly intertwined for the greater good of society. This groundbreaking system not only exemplifies technological prowess but also exemplifies a commitment to a safer and more secure future.

With the increasing need for effective wildlife monitoring and conservation efforts, computer vision technologies have emerged as powerful tools for automating animal detection in diverse environments. This paper introduces an innovative framework for the detection of Indian exclusive animals—species found exclusively in India—employing the YOLOv8 (You Only Look Once) object detection model. The proposed system is reinforced by a meticulously annotated dataset created through the Computer Vision Annotation Tool (CVAT), focusing specifically on the distinctive fauna inhabiting the Indian subcontinent. The YOLOv8 model, renowned for its speed and accuracy, is employed to detect animals in images and video frames. The YOLOv8 model is tailored to detect and classify indigenous animal species, ensuring its adaptability to the unique ecological contexts of India. By harnessing the real-time capabilities of YOLOv8, the system enables efficient and timely monitoring of exclusive wildlife populations, addressing the urgent need for accurate and scalable solutions in conservation efforts. The CVAT annotated dataset encapsulates a diverse array of Indian endemic species, encompassing various habitats and environmental conditions. The manual annotation process ensures precision in delineating bounding boxes around animals, contributing significantly to the enhancement of the model detection accuracy for region-specific fauna. Addressing challenges such as diverse animal poses, complex backgrounds, and varying lighting conditions, our framework demonstrates its adaptability to the specific conditions prevalent in India. This work contributes to the growing body of research in wildlife conservation and monitoring, providing a scalable and accurate solution for automated animal detection. The proposed framework stands as a valuable tool for researchers, conservationists, and wildlife managers dedicated to safeguarding the unique biodiversity of India and its integral role in global ecological balance.

The PM POSHAN mid-day meal program aims to Provide nutritional assistance to students enrolled in pre-primary, primary (classes 1 to 8), and government or government-aided schools.. This initiative ensures that each child receives a specified daily intake of calories and protein, promoting improved health, attendance, and cognitive growth. To effectively manage this program, an integrated tracking system has been proposed, utilizing a QR code system for attendance tracking. The web application facilitates real-time meal consumption data and routine photographic documentation of meals served. Additionally, an AI-based tracker app has been developed for data collection and analysis, empowering stakeholders to monitor the program impact across various scenarios. Nutrition analysis software is employed to plan diverse menus that meet nutritional requirements. Regular health check-ups and maternal involvement further enhance the program effectiveness. Through the implementation of this comprehensive monitoring and evaluation system, the PM POSHAN program ensures transparency, accountability, and the optimal well-being of the students, thereby fostering a healthier and more academically successful school environment.

In today digital age, the legal industry is experiencing a transformative shift towards efficient, secure, and transparent record-keeping solutions. This project introduces a pioneering Blockchain-Based eVault designed to revolutionize the management and preservation of legal records. Leveraging the power of blockchain technology, this system provides an immutable, tamper-proof, and easily accessible repository for legal documents, ensuring the integrity, authenticity, and privacy of sensitive information. By harnessing the capabilities of blockchain technology, the Blockchain-Based eVault for Legal Records addresses the pressing need for secure, transparent, and efficient legal record management. It offers a solution that can significantly reduce the risk of document fraud, errors, and data breaches while simplifying complex legal processes. This project represents a promising step towards the modernization of the legal industry and sets a precedent for the adoption of blockchain-based solutions in other sectors reliant on secure data management and transparency.

Mental health is a critical concern worldwide, with a growing need for effective assessment and support systems. This project focuses on utilizing business intelligence techniques to perform sentiment analysis on mental health-related data. The goal is to gain valuable insights into the emotional well-being of individuals by analyzing their text-based interactions, such as social media posts, forums, or chat logs. Sentiment analysis models are employed to classify text into categories such as positive, negative, or neutral sentiments. Additionally, the project explores the use of emotion detection algorithms to identify specific emotions expressed in the text, such as sadness, happiness, or anxiety. Business intelligence tools and dashboards are employed to visualize and present the sentiment analysis results. These tools enable stakeholders, including mental health professionals, policymakers, and researchers, to monitor and gain insights from the sentiments expressed online in real-time. In order to support early intervention and resource allocation, the project also entails the construction of predictive models that, using sentiment analysis data, can predict future trends in mental health. By leveraging business intelligence techniques for sentiment analysis of mental health data, this project aims to contribute to a better understanding of mental health trends, facilitate timely interventions, and promote overall well-being. The insights gained from this analysis can empower individuals, healthcare providers, and policymakers to make informed decisions and provide more targeted support for mental health challenges.

Tourism is a very flourished and celebrated industry of the modern world as it presents a plethora of advantages to the tourists as well as the facilitators of the industry, a large stake of those being the people native to tour-friendly locations. Each location has an exclusive array of beauty, history, culture, cuisine and community associated with it. Such a diverse range of parameters offers a dimension of complexity to the tour planning process. As the world advances towards automation, the tourist sector also seen to undergo profound changes through integration of various technologies. This paper describes evolution of tourism sector with increase in automation. The study investigates how inculcation of AI technologies can help in reforming the tourism landscape. By synthesizing insights from academic experts and various research papers, this research aims to provide a comprehensive overview of ongoing state of automation and technologies in this domain. Understanding these dynamics is necessary for sustainable development and improvement of automated systems to help grow and enhance the traveler experience.

Bio-inspired computing methods refer to computational techniques that draw inspiration from the principles, processes, and structures observed in biological systems. These methods aim to develop innovative solutions to complex problems by emulating the efficiency, adaptability, and robustness found in nature. Bio-inspired computing encompasses a diverse set of computational techniques inspired by biological systems, ranging from the microscopic scale of cells and molecules to the macroscopic scale of ecosystems. These methods leverage the inherent parallelism, self-organization, and adaptation observed in living organisms to address challenging problems in various domains. Key approaches include evolutionary algorithms, swarm intelligence, neural networks, and artificial immune systems. By mimicking natural processes, bio-inspired computing methods have demonstrated success in optimization, pattern recognition, classification, and decision-making tasks. This abstract provides an overview of the fundamental principles and applications of bio-inspired computing, highlighting its potential to revolutionize problem-solving in diverse fields such as optimization, machine learning, robotics, and beyond.