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Analysis and Implementation of Web Advertisement Optimization Using UCB Reinforcement Learning
Web advertisements are vital in digital marketing for connecting businesses with customers; yet optimizing their placement faces challenges from evolving user preferences. This project explores leveraging the Upper Confidence Bound (UCB) algorithm, a reinforcement learning technique, to automate ad placement strategies based on real-time feedback. Through analysis and implementation, this study investigates UCB's potential for web advertisement optimization. It examines existing methods, delves into reinforcement learning theory, and develops a simulation environment reflecting real-world dynamics. Experimental results comparing the UCB approach against baselines demonstrate its efficacy in improving ad placement and enhancing online advertising campaigns.
IoT Enabled Real Time Low Cost Cardiac Monitor Using LabVIEW
The rapid advancement of the Internet of Things (IoT) and medical technology has paved the way for innovative healthcare solutions. The system incorporates IoT connectivity to enable remote monitoring, data collection, and analysis, enhancing patient care and medical decision making. Cardiac monitoring system used in hospitals to measure and record the cardiac parameters. Vital parameters are transferred within the hospitals either using Wi-Fi or Bluetooth. Range of transmission for such wireless communication is only effective within the hospitals and further transfer to a long distance is limited. The proposed cardiac monitoring system is developed using LabVIEW environment - a versatile Graphical User Interface (GUI). Data acquired through the electrodes are connected to my DAQ Unit (Data Acquisition Unit) which helps to integrate electrodes with computer or PC. Data acquired from the Software is connected to the firebase console and it will help the practitioners to share the data in live and updated. It offers benefits such as early detection of cardiac irregularities such as arrythmia, reduced hospitalization periods, and improved patient outcomes. Additionally, healthcare practitioners and caretakers can remotely access patient data, enabling timely interventions and personalized treatment plans. Since the cardiac monitors are often used by bedside patients, it may introduce bed sores in them. Hence, in addition to these parameters, body moisture level and temperature of the patient are measured using DHT11(temperature and humidity sensor) to ensure the prevention of bedsores. These additional parameters measured are also enabled to view through IoT.
Autonomous Road way Alert System
The "Autonomous Roadway Alert System" design process, which is based on the Waterfall Model for the System Development Life Cycle (SDLC), is discussed in this article. These sign board detection, traffic sign detection, obstacle detection, lane assistant and voice assistant features are the functions of the system tackled through phases of collection and analysis, design, implementation, integration, testing, deployment, and maintenance processes. Emphasis of use of Python-coded programs on Raspberry Pi hardware and YOLOv5 algorithm is given for the purpose of object identification.
Forensic Fingerprint Analysis
Fingerprint evidence found at crime scenes provides vital impressions left when these skin secretions touch surfaces clues in serial criminal investigations. A fingerprint identification system employing deep machine learning and Convolutional Neural Networks (CNNs) could automate the analysis process. Images obtained from various physical and chemical crime scene investiga2tion techniques are entered into the database. However, partial latent prints lifted from scenes are often difficult to classify. The system operates in three phases: preprocessing fingerprint images, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points - ridge endings and bifurcations. False minutiae removal further refines the data. The preprocessed fingerprint data serves as input to train and test the CNN model. As the system persist due to the immutable individuality of fingerprint ridge arrangements [5]. While criminals attempt concealment, fingerprint traces stubbornly remain where other evidence would dissipate [6]. Without these durable biometric markers, crime scenes would often lack the critical traces needed to connect acts to perpetrators [7]. Latent prints lifted from crime scenes first undergo photographic documentation and chemical enhancement techniques in order to visualize trace details [8]. Computer analysis then further improves clarity, isolating minute identifying features known as minutiae [9]. Algorithmic extraction of differentiating traits classifies new latent prints, it continuously incorporates the prints enables training of automated comparison systems using along with confirmed suspect identity matches to improve accuracy. Automated classification and matching facilitate identification. The approach scales as the database grows in size without proportionate growth in human effort. Rapid fingerprint evidence analysis accelerates investigations, potentially solving more crimes by linking serial cases through a central digital repository. I aimed to restate the key technical ideas and flow using alternative vocabulary and phrasing while preserving semantic meaning. Please let me know if you need any clarification or have additional requirements for rephrasing the passage.
Air Quality Monitoring System Using Machine Learning and IOT
Air pollution is a significant and widespread environmental problem worldwide, with numerous researchers focusing on it in light of its impact on human health. One of the most effective ways to raise awareness about the issue and protect public health is through the use of air quality monitoring systems. In many urban areas, air pollution is a major environmental concern. By utilizing real-time monitoring of pollution data, local authorities can make informed decisions about traffic management and other relevant issues. Accurate prediction of air quality (AQ) is crucial for effective decision- making. IoT-based sensors are increasingly being used to dynamically improve AQ prediction, which has traditionally been limited by expensive and low-accuracy techniques. The advancement of machine learning (ML) algorithms has opened up new possibilities for AQ prediction, and recent research has explored the benefits and drawbacks of various methodologies for real-time monitoring and prediction of AQ, as well as the challenges involved.
Tabib: Chatbot for Healthcare Automation with Audio Assistance using Artificial Intelligence
To live a happy and healthy life, people must prioritize their health care. It's not always easy to contact a doctor when you have a health issue, though. The idea is to build a chatbot for healthcare that functions as a virtual assistant using artificial intelligence. The healthcare chatbot system's medical database will be trained using neural networks and natural language processing. The purpose of the healthcare chatbot was to increase access to medical information while reducing overall healthcare costs. There are certain chatbots that function as medical encyclopedias; educating patients about their conditions and helping them take better care of themselves. In the proposed project, a chatbot system converses with patients about their health issues via voice-to-voice and text- to-text and provides a personalized diagnosis based on their symptoms. In addition to text assistance, this chatbot also offers an audio assistance feature that makes it easier for disabled (visually impaired) patients to access the system. This feature eliminates the restrictions that visually impaired patients face with the currently available text-enabled healthcare chat bots. We used the user's current location to locate the specialists nearby and present the information about them. Users can schedule appointments with various physicians in their area. As a result, people will be aware of their health status, receive rapid treatment, and be informed about the best medicines.
Detection of Diabetic Foot Ulcers Using Dense Net Framework
Diabetic Foot Ulcers (DFUs) pose a significant health care challenge due to their prevalence and potential complications. Early and accurate detection of DFUs is crucial for timely intervention and prevention of severe complications. This study proposes an innovative approach for automated DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful class of deep learning algorithms widely recognized for their proficiency in image analysis tasks. The proposed CNN model is trained on a comprehensive dataset of foot images, encompassing a diverse range of DFU types, stages, and conditions. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection. The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management.
Hand Gesture Recognition for Home Automation using Machine Learning and ESP8266 Server
Automation is an evolving field providing immense aid in various fields. Types of automation include Artificial Intelligence, home automation, industrial automation, etc. Smart gesture based automation systems provides application in home automation which can support disabled and elderly persons. Home automation is the use and control of home appliances remotely or automatically. Every day the gap between humans and machines is being reduced. Recently introduced Metaverse and video game consoles like Play Stations gives us the insight of how the future of communication between human and computer would look like. Most of the electronic manufacturers are now focusing on hand gesture based automation. This proposed work uses the tool for the recognition and processing of hand gesture. Deep forest algorithm is used for image processing. At first, the image is captured by the camera and it is processed by OpenCV. This in turn controls the switching of home appliances. The hardware module consists of a camera, AVR AT Mega 328p microcontroller, relay module, power supply and bulbs for demonstration purpose. Supported by the pyFirmata protocol, the microcontroller and the appliances as connected seem less manner. The scope of application for this project is versatile, not limiting its functionality to a fixed application. This gesture based technology helps disabled and elderly people to use smart systems more effectively, reducing cost, man power and time to perform simple tasks for their comfort.
AI Enabled Fitness Analyser
In today's fast-paced world, maintaining a healthy lifestyle is paramount for overall well- being. In line with this goal, we present Diet-Fit, an innovative application designed to revolutionize fitness tracking and dietary planning. By integrating a diet planner and exercise tracker, Diet-Fit aims to empower users to take control of their health and make informed lifestyle choices. Leveraging state- of-the-art technologies such as Mediapipe and OpenCV, the application offers advanced pose detection capabilities, accurately detecting exercise poses, counting repetitions, and tracking calories burned during workouts. Through personalized workout routines and dietary recommendations, Diet-Fit seeks to improve overall fitness levels and promote healthier lifestyle habits among users. By harnessing the power of artificial intelligence and computer vision, Diet-Fit represents a significant step towards enhancing fitness tracking and fostering a culture of wellness in the digital age.
IOT Based Avoid Fire Accident In EV With Multiple Fault Detection
This paper presents a novel approach to monitoring Electric Vehicle (EV) charging ecosystems, crucial for assessing their condition parameters. Leveraging sensor-derived data as a primary information source, this study focuses on the development of predictive models for battery and charging station conditions, particularly concerning voltage levels and associated variables. The core contribution lies in the design and implementation of a multisensory monitoring system tailored explicitly for Battery Management Systems (BMS). Unlike existing proposals, this system is founded upon a set of fundamental requirements that distinguish its design and functionality. Notably, it introduces a variance-based detection scheme for assessing the State of Health (SOH), enabling the prediction of battery degradation and facilitating fault detection within EV systems.
Impact of Cashless Payments on Retail Shops with Special Reference to Hyderabad City
Business crossing the boarders of Country and spreading throughout the Globe. There is vast development of technology new-a-days. Technology is showing a positive impact on Business. Businessmen and customers are most fascinated towards adapting new technology in Business related transactions. Especially in India, after demonetisation the importance for cashless transactions came into existence. Usage of Digital Wallet system often helps in easy and fast financial transaction between the customers and retailers. In this regard the present study focuses on analysing the demographic profile of the respondents, finding out the highest opted digital mode, analysing the uses and problems of cashless payments. The data is collected from various respondents residing in Hyderabad city and it is analysed by using different statistical tools like Spearmen’s rank Correlation and Kruskal Wallis test. It is found that Fear of Fraud is found as the most influencing problem faced by most of the Retailers.
AI Enabled Fire-Detecting Drone Using Deep Learning Model
Wildfires pose significant threats to both human lives and the environment, necessitating advanced technological solutions for early detection and rapid response. This project aims to design, develop, and implement an autonomous drone system equipped with cutting-edge fire detection technology leveraging deep learning concepts. The system's primary objective is to swiftly identify and locate wildfires, enabling timely intervention and mitigation efforts to enhance public safety and environmental protection. The proposed drone system integrates state-of-the-art deep learning algorithms, allowing it to analyze real-time aerial imagery for signs of fire outbreaks with high accuracy and efficiency. Through the utilization of convolutional neural networks(CNNs) trained on vast datasets of wildfire images, the system can recognize distinct patterns and characteristics associated with flames, smoke, and heat sources amidst varying environmental conditions. Key components of the autonomous drone system include advanced sensors such as infrared cameras and multispectral imaging devices, facilitating enhanced detection capabilities across different spectrums. The integration of these sensors enables the system to detect wildfires even in challenging scenarios such as dense foliage or low visibility conditions. Upon detection of potential wildfire activity, the autonomous drone employs intelligent navigation algorithms to swiftly navigate to the identified location for closer inspection and assessment. Real-time data transmission capabilities enable seamless communication with ground-based emergency response teams, providing critical information for prompt decision-making and deployment of firefighting resources.
AI Enabled Drone for Automatic Detection of Human Movements in Disaster Situation
The concept of an innovative drone system designed to revolutionize search and rescue operations in disaster scenarios. This system aims to harness the power of artificial intelligence (AI) for automatic human detection, alongside advanced technologies such as cameras, processors, and payload mechanisms. By integrating these components, the drone system will autonomously survey disaster areas, identifying individuals in need of assistance with precision and speed. Upon detection, the system will generate alarms to alert rescue teams promptly. Furthermore, the drone will be equipped with efficient payload dropping mechanisms, enabling it to deliver essential supplies to remote or inaccessible locations within the disaster zone. Through the seamless integration of AI and advanced technologies, this drone system represents a significant advancement in disaster response capabilities, with the potential to save lives and mitigate the impact of disasters on affected communities.
A Blockchain-Based Proxy Re-Encryption Method for Safe Data Sharing in the Internet of Things
Data sharing has emerged as one of the Internet of Things' most beneficial uses in cloud computing. Even though this technology has been visually appealing, one of its challenges is data security because improper usage of data can result in various harms. We suggest a safe data exchange method for cloud environments in this article: proxy re-encryption. Data owners can utilize identity-based encryption to send their encrypted data to the cloud, where it will be accessible to authorized users through proxy re- encryption construction. An edge device serves as a proxy server to manage demanding calculations since Internet of Things devices are resource- constrained. Additionally, by efficiently delivering cached content in the proxy and utilizing the capabilities of information-centric networking, we enhance service quality and optimize network bandwidth. Additionally, the foundation of our system concept is blockchain, a revolutionary technology that permits decentralization in data sharing. Fine-grained access control to data is achieved, and bottlenecks in centralized systems are lessened.
Freshness of Food Detection Using Iot and Machine Learning
In today's world, food spoilage is a crucial problem as consuming spoiled food is harmful for consumers. Our proposed work aims at detecting spoiled food using appropriate sensors and monitoring gases like carbon monoxide, ammonia and benzene which are released by the particular food item. A micro controller named Arduino that senses this and issues an alert using internet of things, so that appropriate action can be taken. This approach has widespread applications in the food industry, where manual detection methods are prevalent. The preservation of food freshness is critical for ensuring consumer safety and reducing wastage. The proposed machine learning algorithm, Support Vector Machine, has been integrated into this model to estimate the likelihood of food spoilage when purchased from a specific vendor. This will increase competition among retailers to sell more healthy and fresh food and create a safe world for all consumers alike. Through this approach, our project not only enhances food safety but also promotes a culture of freshness and quality in the food supply chain.
Smart Agricultural Land Protection System
Using the Internet of Things (IoT), the Smart Agricultural Land Protection System (SALPS) establishes a network of intelligent sensors to track the condition of farmland. The technology gives farmers the ability to anticipate hazards and make educated decisions by gathering real-time data on variables like temperature, humidity, intrusion, and accidental fires. This includes early wild fire identification, which enables community collaboration for both prevention and response.TheproposedsystemconsistsofESP8266 micro-controller with environmental sensors. This project promotes a future in which environmental responsibility and technology collaborate to guarantee food security for future generations.
IoT Based Smart Cradle for Baby Monitoring System
This initiative introduces the concept of a fully automated nursery designed specifically for infants. The primary goal of this innovation is to alleviate the burdens on exceptionally occupied parents. In today's fast-paced world, working parents often struggle to find sufficient time for infant care. This automated system is engineered to monitor an infant's actions within the nursery and adjust its operations based on those observations. As a result, parents can conserve both their energy and time, as frequent checks on their child are not necessary unless alerted by the system. The functionality of this room is enabled through the integration of various sensors and a microprocessor. These sensors are adept at detecting environmental conditions and the infant’s movements, offering real-time updates and alerts to the parents about their child's status, thereby streamlining childcare.
Identification and Classification of Leaf Diseases using Deep Learning
For agriculture to be sustainable, it is essential to monitor a plant's health and look for diseases. It is quite challenging to manually monitor plant diseases. To improve the agriculture industry and the prosperity of our nation, plant disease must be effectively identified. Several diseases cause the plant's leaves to die. Farmers have a harder time seeing these diseases because it's difficult to care for those plants without knowing about them. The method described in this paper uses Tensor Flow to identify illnesses in plant leaf pictures. CNN training is used on the model proposed to automatically diagnose disease using the object detection API tensor flow. This data is acquired from multiple sources. In order to treat the sickness, the proposed effort will also identify the causes and manifestations of the illness. In the work proposed, to identify plant diseases, sophisticated deep machine learning models based on specific CNN topologies were developed using photos of healthy or diseased plants' leaves. In comparison to existing models, CNN's numerous layers offer a great rating and a 96 percent accuracy level.
Revolutionizing Image Transformation: Unleashing the Power of Machine Learning for 2D to 3D Conversion
This project offers a comprehensive evaluation of recent advancements in Neural Radiance Field (NeRF), a significant development in Computer Vision. NeRF models, known for their neural network-based scene representation and novel view synthesis, find applications in robotics, urban mapping, autonomous navigation, and virtual reality/augmented reality. The survey categorizes findings into architecture and application- based taxonomies, introducing NeRF theory and training via differentiable volume rendering. A benchmark compares key NeRF models' performance and speed, aiming to objectively assess strengths, weaknesses, and applicability across real-life scenarios. Results cover comparisons with photogrammetry in noise level, geometric accuracy, and image baselines across diverse scenes. NeRFs outperform in scenarios with texture-less, reflective, and refractive surfaces, while photogrammetry excels with cooperative textures.
Transcriber AI
This project focuses on the development of application that enables real-time audio- to-text transcription. Leveraging wireless Bluetooth technology, the application captures and transcribes spoken words from both the device's built-in microphone and external Bluetooth devices. "Transformative Bluetooth Communication Device for Inclusive Communication" encapsulates the development of a ground breaking device aimed at overcoming communication barriers. This Bluetooth device integrates a Bluetooth transmitter and a sophisticated microphone, facilitating real-time spoken word transmission. Coupled with a dedicated application powered by advanced speech-to-text technology, the device offers instantaneous transcription, making communication universally accessible. Emphasizing hands-free convenience and portability, the project envisions a transformative bridge catering to individuals with diverse language backgrounds and hearing impairments. Beyond immediate functionalities, the project that’s wide-ranging implications for inclusivity, adaptation to various environments, potential integration with existing platforms, and future research in speech recognition and wearable technology. This abstract outline a visionary venture set to redefine communication paradigms and empower users through technology.