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Design and Analysis of Mars Rover Wheel
This project investigates the design and analysis of Mars rover wheels, crucial components for successful Martian exploration missions amidst evolving technological advancements. Designed to traverse various surface conditions, including rocks and sand, the rover wheel incorporates a unique configuration with a leaf spring, enhancing its performance on challenging terrain. Utilizing advanced CAD and simulation techniques, the proposed design is comprehensively modelled and analyzed, presenting an innovative advancement in Mars rover wheel performance, and marking a significant stride in space exploration.
A Review Paper on CFD Analysis of Solar Dehydration System
This study presents a comprehensive analysis of a solar dehydration system using Computational Fluid Dynamics (CFD).Solar dehydration systems offer a sustainable method for preserving agricultural produce by utilizing solar energy. This review delves into the advancements and applications of Computational Fluid Dynamics (CFD) in the design, analysis, and optimization of solar dehydration systems. CFD has emerged as a powerful tool, providing detailed insights into the thermal and fluid dynamics processes within these systems. By simulating various design configurations and operating conditions, CFD helps in enhancing the efficiency and performance of solar dryers. This review compiles and analyzes recent studies, highlighting the methodologies employed, key findings, and the challenges faced in CFD Modelling of solar dehydration systems. It also discusses the future prospects and potential improvements in CFD techniques to further optimize solar drying processes, aiming for increased energy efficiency, reduced drying times, and better product quality.
Harsenning Deep Learning Techniques for Predictive Cybersecurity: Challenges and Solutions
Predictive cybersecurity - an emerging field that aims to proactively identify and mitigate cyber threats applies deep learning techniques. The application of contemporary deep learning methods, such as Long Short-Term Memory networks (LSTMs), recurrent neural networks (RNNs), and Convolutional neural networks (CNNs), for malware classification, intrusion detection systems (IDS), and anomaly detection is explored in this study. Although these models are effective, they have some drawbacks, including as the constantly changing and dynamic nature of cyber-attacks, the need for large and varied datasets, and the interpretability-impairing black-box nature of deep learning models.This paper offers a hybrid strategy to address these issues by combining classic deep learning models with Generative Adversarial Networks (GANs) to boost data creation and detection performance. To attain improved accuracy and resilience, we also present an ensemble learning framework that combines the advantages of many techniques. The goal of the suggested remedies is to create resilient, adaptable, and explainable cybersecurity systems that are capable of real-time threat detection and prediction. The suggested techniques take advantage of data augmentation and transfer learning to overcome data shortages and enhance model generalisation. Furthermore, the integration of explainable AI techniques endeavours to augment the transparency and interpretability of the models, hence enhancing their dependability for cybersecurity specialists.Through a methodical approach to these problems, the research hopes to push predictive cybersecurity to new heights and offer a more proactive and safe defence against ever-more-advanced cyber-attacks.
Deep Learning Based Network Intrusion Detection System: A Deep Abstract Networks (DANets) Model Approach
The need for frequent updates on Network Intrusion Detection Systems (NIDS) is crucial for safeguarding enterprise data against cyberattacks due to the advancement in the attackers' arsenal resulting from technological advancements. Conventional signature-based NIDS often struggle to keep up with newer types of intrusions due to their sophisticated nature. This has forced the advancement of machine learning-based NIDS, which have shown promising results. There is much recent research on machine learning and deep learning-based network intrusion detection system. Also, there is few important research datasets like UNSW-NB15, KDD CUP dataset, etc published for the use of research community. In this paper, we aimed to assess the effectiveness of a Deep Abstract Networks (DANets) model in network intrusion detection. We used UNSW-NB15 dataset. The accuracy and F1 score of the proposed model were very promising.
Eliminating Harmonics and Voltage Ripples Using Optimal Constructive Recurrent Neural Network on Boost Rectifiers with Unbalanced Grid Sources
Various new methodologies and techniques have been proposed to improve power quality. One of the parameters that affect power quality is harmonics. Voltage and current unbalance is a frequent problem. Electronic devices such as rectifiers and power distribution networks are negatively affected by this imbalance. The current in the grid will be severely distorted. The DC bus voltage will fluctuate or ripple frequently, perhaps twice as often as the frequency. Direct power control (DPC) and voltage-oriented control (VOC) are two examples of control strategies developed for rectifiers. In classical VOC, the voltage and current in the synchronous frame of reference (dq) are controlled using a proportional-integral (PI) controller. However, the PI controller in controlling the VOC current has the disadvantage that when one variable changes, the other variables will also change. PI cannot control itself independently. it is difficult to achieve an accurate linear mathematical model of the system required for PI controllers. In addition, PI controllers often struggle to work satisfactorily on parameter variations, nonlinearities, and load disturbances. in transient conditions, the Optimal Constructive Recurrent Neural Network(OCRNN) controller takes faster time to reach a stable state. when there is a change in the reference dc voltage, PI has a higher overshot voltage and a lower voltage drop than OCRNN and takes longer than the OCRNN controller.
AI-Driven Personalized Medicine: A Frame Work for Tailored Cancer Treatment
Regardless of the variability of the disease and the individual variances among patients, cancer treatment has historically taken a standardised approach, which frequently leads to less than ideal results. By customising treatment plans to each patient's distinct genetic, phenotypic, and clinical characteristics, AI-driven personalised medicine has the promise of completely changing the way cancer is treated. This study suggests a thorough framework for predicting the best course of treatment for each patient by combining multi-omics data, electronic health records (EHRs), and empirical evidence. To analyse complicated datasets, the framework uses machine learning and deep learning models, such as Random Forests, Support Vector Machines, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Model resilience is increased by creating synthetic data with Generative Adversarial Networks (GANs). In order to find possible indicators for therapy response and resistance, AI is also utilised in biomarker identification. Personalised treatment plans are recommended by a decision-support system using AI predictions, and the model's accuracy is continuously improved through feedback loops. This AI-driven strategy dramatically improves patient outcomes, according to preliminary studies, which represents a promising development in tailored cancer care.
AI-Assisted Surgical Robotics: A Review of Current State and Future Possibilities
By improving accuracy, decreasing human error, and permitting less invasive operations, artificial intelligence (AI)-assisted surgical robotics is transforming contemporary surgery. The integration of sophisticated algorithms like computer vision, machine learning, and deep learning approaches is the main emphasis of this paper, which examines the state of AI-driven surgical robotics today. Modern approaches like Convolutional Neural Networks (CNNs) for image-guided navigation and Reinforcement Learning (RL) for autonomous decision-making in surgical tasks are contrasted with traditional methods such as computer-assisted surgery algorithms. Modern AI techniques offer flexibility and continual learning, greatly enhancing results in complicated, real-time circumstances, while traditional systems perform best in organised environments with predefined tasks. The difficulties are also covered, including the necessity for strong legal frameworks, ethical issues, and data protection. Potential future developments include improved human-robot cooperation, fully autonomous surgical systems, and multi-modal data integration for customised surgery. In order to create the path for more successful and efficient surgical procedures, the paper ends by emphasising how AI-assisted surgical robotics has the potential to revolutionise healthcare.
The Effectiveness of a Comprehensive threat Mitigation Framework in NETWORKING: A Multi-Layered Approach to Cyber Security
The rapidly evolving landscape of cyber threats necessitates robust and adaptable strategies to safeguard networking. This study explores the development and evaluation of a comprehensive framework for threat mitigation, integrating advanced methodologies from cybersecurity, risk management, and threat intelligence. Leveraging a mixed-methods approach, including surveys and secondary data analysis, the research assesses current practices and identifies critical gaps in existing frameworks. The proposed multi-layered defense mechanism incorporates proactive and reactive measures, aligning real-time threat intelligence with sophisticated incident response strategies. Key components such as anomaly detection systems, employee training, and continuous security audits are highlighted as essential elements of the framework. Through extensive validation, including empirical tests and case studies, the framework demonstrates its efficacy in enhancing organizational resilience against complex cyber threats. The findings provide valuable insights into the practical application of integrated cybersecurity measures, offering a scalable and flexible solution tailored to the dynamic nature of digital security challenges. This study addresses the critical need for an adaptable and holistic approach to threat mitigation, contributing to the field of cybersecurity with actionable strategies for managing and mitigating digital threats
A comprehensive review on blood brain delivery methods using nanotechnology
A limited amount of the administered medicine can reach the brain. As a result, a higher dose of the drug must be administered, which, as expected, causes several undesirable side effects. For the past few decades, researchers in a range of professions have been working to address this highly important and frequently lethal condition. Nanomedicine is a field of research that has achieved promising findings in recent years. Nanomedicine is the science that combines nanotechnology, chemistry, and medicine. Many various forms of nano-medicine-based drug-delivery systems are currently being investigated with the express goal of improving drug delivery to the brain. This overview compiles and briefly summarizes some of the most significant advances in this crusade. Inorganic nano-particle-based drug delivery systems, such as gold and magnetic nano-particles, are discussed, as are several organic nano-particulate systems. Polymeric micelles and dendrimers are briefly described as organic drug-delivery nano-systems, while solid polymeric nano-particles are thoroughly investigated.
Brain Tumors Detection Using Deep Learning (SGO and SE Algorithm)
Brain abnormality is the harsh illness among humans and is usually diagnosed with medical imaging procedures. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and Diffusion-Weighted (DW) modalities. The combination of the Social- Group-Optimization (SGO) and Shannon’s Entropy (SE) supported Multi-threshold is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES2015 and BRATS (2013 and 2015), and also clinical MRI mages obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with related segmentation trials employed. The performance of the ANFIS is validated with other classifiers, such as Decision-Tree, Random-Forest and KNN. The ANFIS classifier obtained an accuracy of 97.04% on the used DW modality and accuracy of 96.67% on the Flair MR images.
Federated Learning for Edge Computing in REST APIs: Investigate the Feasibility of Employing Federated Learning Techniques to Train Machine Learning Models Directly on Edge Devices Interacting with REST APIs, Minimizing Data Transfer and Enhancing Privacy
This research explores the feasibility of using federated learning techniques to train machine learning models directly on edge devices interacting through REST APIs. This article designs an architecture that facilitates federated learning using REST APIs for communication between a central server and edge nodes. The architecture ensures efficient model distribution, update aggregation, and iterative global model refinement. This research implements this system in a simulated environment to evaluate its performance. Initial results show that federated learning significantly reduces data transfer and enhances privacy without compromising model accuracy. The REST API-based communication model effectively coordinates updates between the central server and edge devices, and privacy-preserving techniques maintain data security throughout the process. This study confirms that federated learning is a viable solution for edge computing, addressing key challenges in data privacy and communication efficiency, and opens pathways for scalable and effective distributed learning systems in real-world IoT applications. Federated learning offers a decentralized alternative, enabling local model training on edge devices with only model updates shared with a central server. This approach minimizes data transfer and enhances privacy.
Crop Recommendation and Crop Yield Estimation Using Machine Learning and IoT
In the contemporary landscape of agriculture, the amalgamation of cutting-edge technologies such as machine learning (ML) and Internet of Things (IoT) has emerged as a transformative force, revolutionizing traditional farming practices. This paper introduces a comprehensive framework for crop recommendation and yield estimation, leveraging the power of ML algorithms and IoT devices to optimize agricultural operations. The dataset utilized in this study was meticulously curated from reputable sources including Kaggle and Google, encompassing a vast array of agricultural parameters ranging from soil characteristics to climate conditions. This rich dataset serves as the foundation for training and validating ML models, enabling robust analysis and prediction. A diverse range of ML algorithms was employed to process and interpret the dataset, with the objective of identifying the most proficient algorithm for crop recommendation and yield estimation tasks. Through rigorous experimentation and comparative analysis, the algorithm exhibiting superior performance across multiple evaluation metrics was meticulously chosen. Moreover, to bridge the gap between sophisticated ML techniques and practical application in the field, a user-friendly interface was developed using Flask, a micro web framework for Python. This intuitive interface empowers stakeholders to seamlessly access the trained model, facilitating real-time crop recommendations and yield estimations based on input parameters. The culmination of this research endeavor presents a paradigm shift in agricultural management, offering stakeholders unprecedented insights into crop selection and productivity optimization. By harnessing the potential of ML and IoT technologies, this framework not only enhances decision-making processes but also contributes to sustainable agricultural practices and global food security.
Identification of Different Medicinal Plants / Raw materials through PCA Using Support Vector Machine and K-Means Clustering Algorithms
This research explores the identification of plants by analyzing their leaves, utilizing digital image processing techniques to examine shape, color, and texture features. Machine learning algorithms are then applied to classify the plants based on these characteristics. The study involves an examination of various image processing methods integrated with machine learning to differentiate between plant species using leaf features captured in images. Image processing serves as the primary approach for categorizing plants based on unique leaf characteristics or specific leaf regions, enabling subsequent identification through image analysis. The research specifically focuses on the identification and classification of plant species based on the analysis of different leaf parts. The paper presents an overview of classification methods applied in plant leaf identification, with Support Vector Machine (SVM) being the primary algorithm used for plant identification and classification. Experimental results demonstrate enhanced accuracy in plant species identification based on leaf features, offering a more efficient and time-saving approach to plant identification
A Study on Employee Retention and Process of Update Colour Process Pvt.Ltd., pallipalayam
Employee Retention is a challenging concern of the organization. This study stressed on Employee Retention strategies. Employees are the assets of the organization. To retain skillful and committed employee in the organization management should take care of employee satisfaction. Find out the reasons employee turnover and overcome this. The purpose of this study in prove how employee retention is essential in this days and age and if the organizations are not wake to the situation and immediate action are not taken to that effect what repercussions lay ahead and how they would affect the organization and the industry. .Employee Retention is a process in which the employees are encouraged to remain with the organization for the maximum period of time or until the completion of the project. It is a strategic tool for the success of the firm. Employee Retention is beneficial for the organization as well as employee. Employees today are different. They are not ones who do not have good opportunities in the hand. As soon as they feel dissatisfied with the current employer or the job they switch to over the next job. A good employer should know how to attract and retains its employees.
A Study on Scope and Challenges in Network Marketing Business
Network Marketing also known as multi level marketing (MLM) or direct selling, is a business model that relies on a network of independent distributors or representations to sell products or services directly to consumers. Network Marketing, is a business model that involves the distribution of products or services through a network of independent distributors. In recent years, network marketing has gained popularity as a flexible and low-cost way for individuals to start their own businesses and earn income. Network Marketing is the Marketing Model to Promote Goods and Services to the end user. This Business model involves the sale of products or services through a network of distributors or independent representatives. The concept of network marketing has been around for decades, but it has gained popularity in recent years due to the growth of social media and the internet. Network Marketing is a marketing model which is used by companies to sell their products and services to the end user, Network marketing business has great impact in countries like USA, Japan, UK, Brazil, Italy, Germany, Canada etc. In all these countries lots of business legends and millionaires emerged as a result of their investment in multi-level marketing. Warren Buffett, Richard Branson, Dexter Yager and Birdie Yager, and even the US president Donald Trump are the spokesmen of network marketing as they derived a part of their wealth through the investment in network marketing. Network Marketing offers income potential and flexibility, but it also poses challenges such as a high failure rate, recruitment pressure, and regulatory scrutiny. Before getting involved in network marketing its essential to thoroughly research the company, understand the compensations plan, and consider your own skills and recourses. It’s not a guaranteed path to financial success, and success often depends on personal commitments, ethics, and specific MLM company’s reputation and product. In recent, the landscape of entrepreneurship and income generation in India has witnessed a remarkable transformation, with network marketing emerging as a prominent business model. India, a nation renowned for its diverse demographics, cultural richness, and rapidly expanding economy, presents an intriguing and complex arena for network marketing ventures. The industry has faced numbers of challenges, ranging from its legality and allegations of fraudulent operations to the economic diversity that characterizes the Indian parameters. This review paper embarks on a comprehensive exploration of the network marketing landscape in India, aiming to look over scope and challenges of this industry. This paper seeks to provide a nuanced understanding of an industry that has. As India's middle class and youth population continue to seek innovative income opportunities, it is paramount to assess the merits and pitfalls of network marketing. This review endeavours to contribute to the discourse by offering a balanced perspective that considers both the opportunities for financial empowerment and the potential risks for participants in this business model. Network Marketing has a dynamic nature, a sector constantly shaped by technological advancements, shifts in consumer behaviour, and evolving regulations. It strives to offer insights that can aid policymakers, entrepreneurs, scholars, and potential participants in making informed decisions regarding network marketing endeavours in India. We delve into the historical backdrop, regulatory landscape, socio-cultural factors, challenges, success stories, and future prospects that define network marketing in India. Through rigorous analysis and critical examination, we aim to shed light on the intricate tapestry of an industry that continues to be a significant player in the Indian entrepreneurial ecosystem. Network marketing business has both scope and challenges in the marketing environment. The organisation has potential to reach all the population of the country but also have some challenges to grow in the nation.
Autonomous Robotic System for Identifying and Extinguishing Fire
Putting out fires is a hazardous profession with a high death rate. The new approach to preserving both the environment and human life is robotics. This work suggests an autonomous robot system that uses a flame sensor to invariably detect and put out fires. The Arduino UNO, water pump, servo motor, motor driver, relay module, Bluetooth HC-06 module, and flame sensor are all part of this project. In addition, the robot's mobility is controlled by commands transmitted to the receiver via the push Bluetooth app on the transmitting end. The robot moves and the fire is doused with water thanks to the motors that are attached to the microcontroller. The robot body has a water pump and tank that are automatically identified by the infrared. The flame sensor and the entire system are managed by a microprocessor from the ATMEGA328 series. The microcontroller and a motor driver integrated circuit (L298N) are interfaced so that the controller may drive the motors. The robot can therefore sense fire at a distance. Around 5.11 cm is the average length for detecting flame, and 300 cm is the typical length for Bluetooth transmission. It might lessen constraints brought on by human mistake and fire extinguishing tasks.
Use of Digital Technology to Calculate Water Footprints for Different Daily Use Items
Water security is a global concern for utilities due to urbanization and climate change. Although water utilities are directly affected by climate change, greenhouse gas emissions are also a result of their activities. Urban water cycles are becoming more sustainable as a result of the efficient operations of utilities made possible by digital technologies. Nonetheless, there is still a dearth of research on the global advancement of digital water transformation. Here, we report on the findings of an online survey that 64 utilities from 28 countries participated in to learn more about the digital transformation's effects on the water utility industry, its drivers, and the main technologies that make it possible. We discovered that the water distribution system serves as the gateway for additional digital technology adoption throughout the entire urban water cycle. In addition, the main forces behind the adoption of technology are economic gains, governmental regulations, and hydroclimatic conditions. We identify areas for future research aimed at improving our understanding of the role that company culture, consumer involvement, and regulations play in effective digital transformation, based on the survey results.
A Study on the Marketing Strategy for a New Product Development and Launch in the Market
In today’s market careful planning and a good product strategy are essential to help you succeeding in the market. Companies everywhere are competing more than ever to develop new and unique products or services and gain a higher position in the market. Companies develop a high number of new products every year. Most of these products fail to secure competitive advantages and end up being just another high cost for the company. The aim of this thesis is to develop a product development and launch process for Company X, Finnish supplier of roll handling equipment for the paper industry. The need for this thesis came during the author´s working experience with the company. Company X is a young company that creates innovative products for the industry. Developing new unique products that offer several benefits to the customers is the company mission and at the same time one of the main strengths. As the new product development is a continuous process and Company X is growing its product portfolio continuously, the commissioner felt the need instructuring the product development and launch process in a well-defined step-by-step process that is easy to follow and communicate internally. The author’s knowledge and experience gained during the studies in international business and working with in the paper industry can be utilized to help Company X in building this process. New products are very essential for the corporate success. Innovation is a must if you want to continue competing in the market. However the new product development is one of the riskiest processes that require focus and high investments. Several studies have been done in the successful product launch cases, and success drivers have been discovered. Corporations are using a structured idea-to- launch processes which help them manage implementing these success drivers and reduce the risk of product launch fail.
A Study on Difficulties Faced by Entrepreneurs While Exporting Products with Reference to Erode District
Difficulties faced while exporting are challenging and can delay the exporting process a lot, because of which the importer can easily switch to another exporter, even though having experience of exporting of more than 5year with frequency of regular export, major difficulties faced by exporters are complex Export procedure as the export procedure includes some of the complex paper-work sometime the quality also played major role in export as the standard set by the importing country does not match exported goods quality. Language was also one of the barriers for communication which leads to misleading of information’s. Letter of credit and documents against payment was most trusted method of payment of majority of the respondents, for the respondents who exports raw material are not more affected with cultural difference whereas exporters with finished goods are affected with cultural difference.
IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service
Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.