Search

Drones are becoming more and more common, which has advantages but also concerns. They can be used to support illegal operations like drug trafficking and endanger places that are important to security. While advances in sensor technology haven't produced reliable answers in the literature, current drone detection and neutralization methods frequently require previous detection and categorization. Using radio frequency (RF) signals and a frequency signature-based deep learning model, this work promotes an environmentally friendly and multidisciplinary method of drone identification and categorization. Using commercial drones, we generated a novel drone RF dataset and thoroughly evaluated two-stage and combined detection-classification frameworks, assessing their efficacy in simultaneous multi-signal and single-signal scenarios. According to our investigation, the Deep Residual Neural Network (DRNN) framework achieves classification performance that is equivalent to that of the YOLOV5 framework, but it offers improved detection performance in multi-signal settings when compared to traditional Goodness-of-Fit (GoF) spectrum sensing. This study opens the door for multidisciplinary efforts to address new security concerns by showcasing the possibility of long-term, data-driven drone detection methods.

The “Sustainable Smart Safety Helmet for Coal Miners” paper aims to enhance the safety and well-being of coal miners by implementing an innovative monitoring system. The paper utilizes the ESP32 microcontroller, integrated with a suite of sensors, including DHT11 for temperature and humidity measurement, MQ135 for gas detection, IR Sensor for helmet status monitoring, and a Heartbeat Sensor for real-time pulse rate tracking. A Buzzer is also integrated to provide immediate audible alerts when potential hazards are detected. The system continuously collects sensor data in real-time, which is then processed and analysed within the ESP32 microcontroller. The data is transmitted to a user-friendly mobile application developed using Blynk. Through the Blynk app, miners and relevant personnel receive critical information regarding environmental conditions and the miners' vital signs. In case of any hazardous conditions, the system generates prompt alerts, triggering the Buzzer and sending notifications to the app to ensure timely responses to potential threats. By combining advanced sensor technology, real-time data monitoring, and swift alert mechanisms, this paper endeavours to safeguard the lives of coal miners in hazardous working environments, providing them with a smart safety solution and empowering them to work with increased confidence and security.

Our paper focuses on the implementation of a decentralized cloud storage system using the IPFS (InterPlanetary File System) technology. The project aims to provide an alternative to centralized cloud storage by decentralization, enhanced security, and user control over data which is a more sustainable solution than the existing system. In a centralized cloud storage system, data is stored on a single server controlled by a service provider. This centralized approach poses potential risks such as single points of failure, data breaches, and reliance on the trustworthiness of the provider. In contrast, our decentralized cloud storage system distributes data across a network of interconnected nodes. Each node stores a portion of the data, ensuring redundancy and availability. By leveraging IPFS, which utilizes a distributed hash table and content- addressed storage, our system ensures data integrity, fault tolerance, and censorship resistance. Users of our system have greater control over their data. They can participate in the network by contributing their storage resources and accessing their files securely. The project implementation includes features such as file upload, download, retrieval based on IPFS hashes, and user authentication. It also provides functionalities like displaying connected peers, managing file metadata, and enabling file deletion. Overall, our project showcases the potential of decentralized cloud storage, exemplifying its benefits and illustrating how it can be implemented using IPFS technology.

The primary objective is to construct a sustainable machine-learning model that utilizes multiple variables to forecast the success of a startup enterprise. It incorporates a Flask application for creating a user-friendly interface, where users can input specific parameters related to a startup, such as financial metrics, industry sector, and location. These inputs are then passed through a sustainable machine learning prediction model, which has been trained on a comprehensive dataset of startup information. The model employs sustainable advanced algorithms to evaluate their startup ventures' potential success. Through the development and deployment of the Flask application and the integration of sustainable machine learning prediction model, this model contributes to the field of startup analysis and decision-making. It offers a sustainable and efficient solution for predicting startup success, empowering users to make data-backed decisions and optimize their resource allocation.

Water pipe spillage, especially in drinking water frameworks, could be a noteworthy sustainable issue universally, driving to the wastage of valuable assets. Recognizing the significance of water for all shapes of life, it gets to be pivotal to create sustainable arrangements to address this issue. The aim of this venture is to form an Online of Things (IoT) based framework for water spillage discovery utilizing water stream sensors and an Arduino board. The concept behind this extend is to degree the sum of water provided from the source and compare it with the amount of water gotten at the destination. In case these two measured values are rise to, it shows that there’s no water spillage within the supply pipes. Be that as it may, in the event that there’s a error between the supplied and gotten water volumes, it recommends that there’s a water spillage some place within the pipeline framework. Begin by getting the specified materials, such as water stream sensors, an Arduino board (such as Arduino Uno), jumper wires, a control supply, and other supporting elements. Interface the water stream sensors to the Arduino board utilizing jumper wires. Guarantee appropriate wiring and network to precisely the degree the water flow. Create the computer program: Utilize the Arduino IDE (Coordinates Advancement Environment) or a appropriate programming environment to type in the code for your model. The code ought to empower the Arduino board to perused sustainable information from the water stream sensors and perform the fundamental calculations to compare the provided and gotten water volumes. Conduct calibration tests to guarantee exact estimations. You'll be able do this by comparing the readings from the prototype system with known water amounts to set up a reference point. Screen the estimations and watch if any disparities between provided and gotten water volumes show potential leakage. Implement real-time monitoring and cautions (discretionary): To improve the system’s functionality, we'll be able to consolidate real-time observing and cautions. It is worth noticing that this model show serves as a confirmation of concept. Executing such a framework on a bigger scale would require contemplations such as arrange network, information transmission, and integration with a centralized observing framework.

While the patient is asleep and unable to reply, vital bodily processes like breathing and blood circulation are still largely functioning. Comas can emerge as a result of injuries like head trauma or as a side effect of underlying disorders. When the patient’s recovery will occur is not predictable. These individuals require the best treatment and constant monitoring for sustainable care. In this work, patient data is continuously monitored and recorded without human intervention, contributing to a sustainable healthcare approach. Unexpected variations in the patient’s usual range of physical parameters are detected, whether they are awake and blink their eyelids or experience abrupt changes in body temperature. Additionally, the system has the capability to instantly alert a medical expert, ensuring timely intervention. The inclusion of a tilt sensor allows for the detection of even subtle movements, further enhancing the ability to provide sustained monitoring. A medical professional can continuously monitor the patient’s condition as it is displayed on an LCD screen, promoting sustainable and effective healthcare management.

The brain is a body organ that controls exercise of the relative multitude of parts of the body. Conceding robotized mind tumors in MRI (Magnetic Reverberation Imaging) is a confounded assignment given size and area variety. This strategy decides a wide range of malignancies in the body. Past techniques devour additional time with less accuracy. A manual assessment can be mistaken because of the degree of intricacies engaged with cerebrum tumors and their properties. However, the above proposition isn’t appropriate for mind tumors because of colossal varieties in size and shape. Our proposed strategy to magnify arrangement performance. First, the expanded tumor district using picture enlargement is utilized to return for capital invested rather than the unique tumor area since it can give hints for tumor types. Second, expanded tumor locale split into progressively refined ring structure subregions. With three-component extraction approaches, employing photographs for information augmentation and rotating photographs at various angles, evaluate the performance of the suggested strategy on a large dataset. Utilizing Convolutional Recurrent Neural Network (CRNN), grouping of the tumor into three categories and thus give a virtual portrayal of exact value.