This study focuses on the production and development of agricultural robots. The main agricultural jobs that robots are utilized for are harvesting, plowing, leveling, and seeding. This robot is meant to take the place of human labor. Due to the complexity of the jobs involved and the lack of need for many repetitive tasks, the agriculture industry is lagging behind other industries in adopting robots. The robot in this project is capable of autonomous plowing, leveling, seeding, determining the soil's moisture and humidity content, watering plants, and applying pesticides. It also provides manual control as needed.

Read More...

Apart from nurturing the agriculture sector for million people in India, enabling a farmer's lifestyle can be done effortlessly. Agriculture enables an individual to have a livelihood and a means of lifestyle. Farmers can benefit from having the right guidance throughout crop selection and managing their irrigation system optimally alongside fertilizer usage[1][6]. For these farmers and more, the ASEF financial web Application is specifically made to tackle all these concerns. It gives Ergonomic suggestions of crop recommendations based on what was previously grown, and also assists with fertilizers needed for specific crops[9][10]. Like every other application, it fetches the current weather conditions so that the farmer can know whether it is optimal or not and can plan his crop rotation accordingly[12]. Not only does ASEF assist with growing climatic conditions, it also helps in educating the user regarding smart farming and best practices alongside addressing crop management issues[5][7]. Through integration with sophisticated online and offline data sources, the platform provides encapsulated information to boosting the sustainability of farming. All of these provided by the recommendation system will enhance the productivity, decision making and foster the sustainable food farming framework utilized in the farmers ecosystem[3][4].

Read More...

Artificial intelligence (AI) is rapidly changing several aspects of advanced manufacturing, bringing unparalleled opportunities for efficiency improvement, quality control, and innovation. This review article presents a balanced overview of the emerging role of AI in the advanced manufacturing context, with a particular emphasis on its revolutionary influence on thermal spray coating technologies. We discuss the integration of AI techniques, such as machine learning, computer vision, and natural language processing, in the thermal spray process chain, from process optimization and material selection to defect inspection and predictive maintenance. We also delve into more general applications of AI across advanced manufacturing areas, such as additive manufacturing, robotics, and supply chain management, with special emphasis on the synergistic opportunities and future trends of AI-aided industrial progress. This review will enable researchers, industry stakeholders, and policy makers to appreciate the latest state-of-the-art and the potential of AI in revolutionizing the future of manufacturing, with thermal spray coating being a good case study for its far-reaching influence.

Read More...

Jigs are specialized tools used in manufacturing to control the location and motion of other tools. This paper presents the design methodology for a jig intended for the machining of an elbow joint component used in industrial pumps. The aim is to enhance precision, reduce production time, and improve operational efficiency. The study addresses the design requirements, material selection, and fixture layout, concluding with an analysis of the jig's effectiveness based on manufacturing parameters.

Read More...

Urbanization has led to the reduction of green spaces, increased waste generation, and heightened environmental concerns. Vertical bottle gardening emerges as a sustainable "waste-to-wealth" innovation, addressing these challenges by repurposing plastic bottles for urban greening. This research explores the feasibility, benefits, and environmental impact of vertical gardening using discarded plastic bottles in compact urban settings. The study highlights how this approach not only reduces plastic waste but also promotes food security, enhances aesthetic appeal, and improves urban microclimates. Through a combination of experimental setup and qualitative analysis, the research demonstrates the potential of vertical bottle gardens as an eco-friendly, cost-effective, and scalable solution for sustainable urban development. The paper advocates for community-based adoption and integration into urban planning strategies to foster greener, cleaner, and more resilient cities.

Read More...

Haze significantly degrades the visual quality of videos, hindering the performance of various vision-based applications. While deep learning techniques have shown promise in video dehazing, many existing methods apply a uniform dehazing effect across the entire video frame, often leading to over-enhancement in clear regions and insufficient dehazing in hazy regions. This paper presents an adaptive video dehazing method that addresses this limitation by incorporating a Haze Density Estimation Module. This module analyzes the input video frames to estimate the spatial distribution of haze. The estimated haze density maps are then used by a modified Region Attention Module to adaptively focus the dehazing process, applying stronger dehazing in denser regions and preserving details in clearer regions. Experimental results demonstrate that the proposed method effectively removes haze while preserving image details, outperforming existing global dehazing approaches both qualitatively and quantitatively. This adaptive approach offers significant potential for enhancing video clarity in applications where haze is non-uniformly distributed.

Read More...

Career guidance has long played a vital role in professional development, enabling individuals to navigate the complex job market and align their skills with suitable opportunities. Traditional counseling methods, often reliant on human expertise, are limited by subjectivity, restricted accessibility, and an inability to process large-scale career data effectively. With rapid advancements in Artificial Intelligence (AI), career guidance is undergoing a transformative shift, offering data-driven, personalized, and scalable solutions for job seekers. This research explores how AI technologies—such as Machine Learning (ML), Natural Language Processing (NLP), predictive analytics, and AI-powered chatbots—are revolutionizing career guidance. By analyzing vast datasets, AI systems can identify skill gaps, forecast job market trends, and recommend career pathways aligned with individual competencies and aspirations. AI-driven platforms also utilize dynamic profiling, which continuously updates based on a user's evolving skills and experiences, ensuring relevant and precise recommendations.

Read More...

Multi-level inverters (MLIs) have become a crucial component in modern power electronics, particularly in electric vehicle (EV) applications. Compared to conventional two-level inverters, these advanced inverters offer superior performance by delivering output waveforms with multiple voltage levels, reduced total harmonic distortion (THD), and enhanced power quality. The reduction in THD leads to decreased electromagnetic interference (EMI), lower losses, and improved efficiency, making MLIs the preferred choice for EV powertrains. This research introduces an innovative 7-level inverter topology to minimize component count while maximizing efficiency and reliability. Traditional MLI configurations, such as cascaded H-bridge, diode-clamped, and flying capacitor topologies, often suffer from significant switching losses, many switches, and increased complexity.

Read More...

This DTI project titled "Weather Buddy” aims to help students manage their daily finances efficiently. The primary objective is to develop a simple, user-friendly mobile application that allows students to log their expenses, categorize them, and monitor their spending habits. The project addresses the common issue of overspending among students due to the lack of structured budgeting tools. Nowadays we face a huge problem that knowing real weather status instantly in such a place we need to know it.

Read More...

The rise of infectious diseases around the world emphasizes the need for cutting-edge instruments to improve early identification and prevention. An AI-powered medical chatbot that predicts infectious diseases based on user-reported symptoms and medical history is proposed in this study. Through real-time interaction and the integration of machine learning (ML) and natural language processing (NLP), the chatbot correctly detects infections and suggests the best course of action, including diagnostic testing or medical advice. The program, which was trained on a wide dataset, can efficiently identify between disorders that have similar symptoms. It creates customized health evaluations and provides prompt, situation-specific guidance. The system also helps with public health monitoring by offering anonymised data for outbreak detection and epidemiological analysis. High prediction accuracy in the experimental results indicates the potential of AI-driven solutions to enhance public health outcomes and healthcare delivery.

Read More...

Additive Manufacturing (AM), commonly known as 3D printing, has emerged as a transformative technology with significant implications across industries. This research paper presents a comprehensive review of the current state of additive manufacturing, focusing on its diverse techniques, novel materials, and wide-ranging applications. The paper systematically examines various AM techniques, including powder bed fusion, vat polymerization, material extrusion, binder jetting, and directed energy deposition, highlighting their underlying principles and technological advancements. The exploration of materials in additive manufacturing encompasses polymers, metals, ceramics, composites, and biomaterials, each with unique characteristics and challenges. This paper delves into the ever-expanding applications of additive manufacturing industries such as aerospace, healthcare, automotive, consumer goods, and architecture have embraced AM for rapid prototyping, customized production, and complex geometries that were previously unattainable. The paper outlines specific use cases and showcases how AM is driving innovation and enabling new design paradigms across sectors. It is evident that additive manufacturing is reshaping manufacturing paradigms and fostering unprecedented opportunities for innovation across industries.

Read More...

Data cleaning is widely acknowledged as a critical step in preparing datasets for machine learning (ML). This review examines how data cleaning influences ML model accuracy by synthesizing recent literature. We survey systematic studies and empirical experiments addressing cleaning tasks (e.g., handling missing values, label errors, duplicates) and their effects on classification, regression, and clustering models. Key papers include the CleanML benchmark study, a broad systematic review of data cleaning for ML, an empirical analysis of data quality dimensions, and the COMET system for prioritizing cleaning efforts. Overall, we find that targeted cleaning generally improves accuracy, but gains vary by error type, data context, and resource constraints. For example, imputing missing values or correcting label errors often enhances performance, whereas removing duplicates or fixing minor inconsistencies may have little or no effect. We highlight limitations such as high cleaning costs and unpredictable benefits in real-world settings, and discuss strategies like automated tools and iterative methods (e.g., COMET, ActiveClean) to focus effort on the most impactful data issues. Our synthesis points to a “data-centric” ML paradigm: effective cleaning must be guided by downstream tasks. We conclude with practical insights (e.g., prioritize feature/label accuracy) and future directions, including tighter ML–cleaning integration and automated, cost-aware cleaning processes.

Read More...

Agriculture optimization system is transforming the agricultural sector by boosting productivity, optimizing input usage, and reducing ecological footprints. This study introduces a holistic framework that leverages machine learning methodologies for forecasting crop yields and managing agricultural resources, with the goal of enhancing decision-making in farming practices. We investigate several machine learning approaches, such as regression models and deep learning networks, to estimate crop outputs using historical records, soil properties, and climatic data. Additionally, we present an adaptive resource distribution mechanism that efficiently allocates water, fertilizers, and other inputs based on yield projections and real-time field information. The outcomes reveal notable improvements in prediction accuracy and resource utilization, offering a strong pathway toward environmentally friendly and data-driven agriculture.

Read More...

Traditional movie recommendation systems often struggle with limitations such as the cold start problem, static algorithms, and reliance on explicit feedback, leading to suboptimal personalization. This project proposes a human disposition-based recommendation system that integrates Singular Value Decomposition (SVD), K-Nearest Neighbors (KNN), meta-learning, and reinforcement learning to dynamically adapt to user preferences. By incorporating emotional and behavioral factors, the system enhances personalization and responsiveness. Performance evaluations demonstrate significant improvements, with an accuracy of ~85% and reduced computational complexity, surpassing traditional systems' typical accuracy of 70–75%. This approach promises a more engaging and efficient recommendation experience.

Read More...

Today, in the present era of technological advancement due to rapid urbanization and industrialization, the sound generated from the source is now becoming the noise mainly in urban environments. For monitoring and controlling noise pollution, Central pollution control board (CPCB) has classified urban environment into four zones namely Residential Zone, Commercial zone, Industrial zone and Silence zone. Silence zone being the noise sensitive zone is now getting noisier, mainly Hospital premises in a manner that noise pollution level in hospital areas is crossing the permissible standard limit defined for industrial zone. Hospitals in the silence zone is among the areas which need attention regarding controlling and managing noise pollution sources to comply with the standards of CPCB (2009).

Read More...

This research explores Explainable Artificial Intelligence (XAI), focusing on techniques that make complex machine learning models transparent and understandable. As AI systems become more widespread, ensuring that their decisions are interpretable is essential for trust, fairness, and ethical deployment. This study reviews recent advancements in XAI, highlights major limitations faced by researchers, discusses strategies used to overcome these issues, and presents key findings to pave the way for future innovations. Furthermore, it introduces novel perspectives on integrating XAI with evolving AI technologies, including Federated Learning, Reinforcement Learning, and Autonomous Systems, to enhance model interpretability and trustworthiness.

Read More...

The increasing demand for reliability and efficiency in industrial applications necessitates advanced health monitoring systems for induction machines. Traditional diagnostic methods often fail to handle complex machine dynamics and diverse fault conditions effectively. Computational intelligence (CI) techniques, including artificial neural networks, evolutionary algorithms, fuzzy logic, and deep learning, offer robust solutions for fault detection, diagnosis, and predictive maintenance. This paper reviews the role of CI-based approaches in analyzing motor performance, identifying faults such as rotor bar defects, bearing failures, and stator winding issues, and predicting failures before they escalate. Furthermore, the integration of CI with IoT-enabled smart monitoring systems, enabling real-time data processing and decision-making can also be explored. The study highlights the advantages of these methods in enhancing fault classification accuracy, reducing maintenance costs, and improving system longevity. This paper suggests that CI-based health monitoring is a transformative step towards autonomous and intelligent condition-based maintenance of induction machines.

Read More...

The jewellery e-commerce project aims to create an online platform where customers can browse, select, and purchase a variety of jewellery products conveniently from their homes. The project will focus on providing a user-friendly interface, secure payment gateways, and a diverse range of high-quality jewellery items to cater to different tastes and preferences. Leveraging the latest technology in e-commerce and digital marketing, the platform will offer personalized recommendations, seamless navigation, and efficient customer support to enhance the shopping experience. Additionally, the project will prioritize aspects such as inventory management, supply chain optimization, and customer engagement strategies to ensure operational efficiency and sustainable growth. By combining technological innovation with the timeless allure of jewellery, the e-commerce project aims to establish itself as a premier destination for enthusiasts and aficionados alike, fostering a vibrant community around the artistry and elegance of fine jewellery. Additionally, the platform places a strong emphasis on transparency and ethical sourcing practices, working closely with suppliers to ensure that all jewellery items meet stringent quality standards and adhere to responsible manufacturing practices. Data driven insights play a cruciamarketing strategies and enhancing the overall customer experience.

Read More...

When a vehicle is decelerating, its kinetic energy is momentarily saved and then repurposed as kinetic energy by the regenerative braking system. To recover energy that is frequently wasted in today's world due to the energy crisis and the ensuing resource depletion, certain technology is required. In other words, the regenerative braking system is one of those practical innovations. One step toward lowering the usage of fossil fuels is regenerative braking. When braking, a significant quantity of energy is wasted as heat. Instead of wasting this energy, a regenerative braking system seeks to use it. Through this method, the electric traction motor recovers energy lost during braking by using the momentum of the vehicle. This is in contrast to the traditional braking system, which uses dynamic brakes or converts extra kinetic energy into undesirable heat that is squandered as a result of brake friction.

Read More...

Artificial intelligence (AI) is rapidly revolutionizing industries across various sectors, driving innovation in areas ranging from automation and data analytics to the development of novel materials and processes. This paper explores the transformative potential of AI-driven innovation, highlighting its impact on diverse fields. While examining the broad spectrum of AI applications, a specific focus is placed on the critical challenge of material degradation due to corrosion in industrial systems. To illustrate AI's indirect yet significant role in advancing solutions to such challenges, this paper delves into the application of thermal spray coatings for combating corrosion in boiler steel. By optimizing coating design, deposition processes, and predictive maintenance strategies, AI can contribute to the enhanced performance and longevity of materials in demanding environments. This exploration demonstrates how AI, even when not directly controlling the coating process, can be instrumental in improving material selection, process efficiency, and ultimately, the resilience of critical industrial infrastructure. The insights presented underscore the pervasive influence of AI in fostering innovation and addressing long-standing engineering challenges through advanced material science and surface engineering principles.

Read More...

Mobile Adhoc Networks (MANETs) are made up of a collection of wireless mobile nodes that communicate with one another on a dynamic basis without the need for a wired backbone network or a stationary base station. Ad hoc networks don't offer safe boundaries. Additionally, the ad-hoc network might offer some incursion. Solutions created on the spot for a particular goal are referred to as ad hoc. In an ad hoc network, computing nodes—which are typically wireless—serve as routers, sending messages between nodes within their wireless communication range. One of the typical issues with all networks is intrusion; in the case of an ad hoc network, we encounter the same issue. Security is necessary when there are many nodes in a dense sensor network and some critical data is being transmitted over the network. However, it is never simple to declare a network to be intruder secure when there is a "Man in the Middle." It is challenging to solve the issue, even if the hacker is aware of the routing methods or how they are implemented. We offer a method for moving data from a different way than the typical one.

Read More...

The integrity and safety of aging concrete infrastructure have become paramount due to environmental degradation, overloading, and time-dependent deterioration. This paper explores the application of the rebound hammer test—a prevalent non-destructive testing (NDT) method—to evaluate the surface hardness and estimate the compressive strength of in-situ concrete in the context of retrofitting. Through controlled testing on aged buildings, correlation with core tests, and consideration of influential factors such as carbonation and surface conditions, this study evaluates the effectiveness, precision, and limitations of the rebound hammer method. The discussion also extends to optimization strategies for test application in retrofitting workflows, emphasizing its role as a screening tool and its integration with other NDT techniques. The findings reinforce the rebound hammer test's relevance for sustainable infrastructure rehabilitation and propose future research directions to enhance reliability and applicability.

Read More...

The relentless degradation of metallic materials due to corrosion poses significant challenges across diverse engineering applications, ranging from the intricate environment of the human body to the harsh operating conditions within industrial systems. This paper provides a comprehensive overview of corrosion mechanisms affecting metals, highlighting the critical need for effective surface treatments to enhance their longevity and performance. While a primary focus is placed on the corrosion behavior of metallic biomaterials and the surface modifications employed to improve their biocompatibility and resistance to physiological environments, this review also extends to explore the corrosion challenges encountered in boiler systems. Specifically, it examines the application of thermal spray coatings as a prominent surface treatment strategy for mitigating corrosion in boiler steel exposed to high temperatures and aggressive chemical environments. By drawing parallels between the demands for corrosion resistance in medical implants and boiler components, this paper aims to provide a broader understanding of the principles governing material degradation and the versatility of surface engineering techniques in combating corrosion across seemingly disparate fields. The insights presented underscore the importance of tailored surface treatments in ensuring the long-term reliability and safety of metallic components in critical applications.

Read More...

With the introduction of fibers into concrete, a beneficial method that has evolved to increase the mechanical and durability features of concrete products has emerged. Between the many different types of fibers, Sisal fiber and Polypropylene (PP) fiber have garnered a substantial amount of interest owing to the distinctive qualities that they possess. Sisal fiber, which is a natural fiber that is produced from the Agave Sisalana plant, is biodegradable, lightweight, and cost-effective. Polypropylene fibers, on the other hand, are synthetic fibers that provide greater resistance to chemical assault and water absorption. In this study, a detailed assessment of the mechanical and durability characteristics of Sisal fiber reinforced concrete and PP fiber reinforced concrete (SFRC and PPFRC) is presented. Our primary emphasis is on the influence that the kind of fiber, its content, its length, and its orientation have on the performance of concrete. We investigate both laboratory research and actual applications. There is also a discussion of the durability elements, which include resistance to corrosion, freeze-thaw cycles, and abrasion. According to the results, both sisal and polypropylene fibers have the potential to considerably increase the mechanical qualities of concrete, including its toughness, compressive strength, and flexural strength, as well as its durability, characteristics that include resistance to water permeability, chemical assault, and other similar factors. Nevertheless, there are still areas that need more investigation, such as the issues of fiber dispersion and the optimal fiber dose.

Read More...

This DTI project titled "ECHO GLOW: A Sound-Activated Lighting System" introduces an innovative approach to controlling lighting through sound. It addresses the common need for accessible and intuitive lighting solutions in various environments. The primary objective is to develop a device that responds to a simple clap, providing a hands-free and user-friendly alternative to traditional light switches. Echo Glow utilizes a sound sensor, a microcontroller, and a relay circuit to activate an LED light source upon detecting a clap. Key features include reliable sound activation, adjustable sensitivity, and potential for integration with different lighting systems. This project demonstrates the feasibility of using sound as an effective control mechanism and highlights its potential for enhancing user convenience and accessibility.

Read More...