IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

A novel approach to analysis and prediction is provided by the internet of things-based time monitoring and prediction system using wireless sensor networks (WSN) and machine learning techniques (ML). To give accurate meteorological data in real time, the integrated system uses IoT, WSN, and ML. Making informed decisions requires these insights. Includes strategically positioned infrared points that are used to gather meteorological information, such as temperature, humidity, pressure, and wind speed, among other things.The machine's automatic data processing methods are then used in a central processing unit to collect and analyse the data. By seeing patterns and drawing diagrams utilising previously collected data, ML models are able to comprehend intricate temporal dynamics. An important development in this system is its predictive capabilities. Artificial intelligence has the processing power to precisely forecast short-term weather patterns, enabling the rapid transmission of warnings for extreme localised events and the reduction of potential dangers.The combination of historical data, real-time sensor inputs, and automated analysis produces the predictive potential. The "Internet of Things" architecture used to develop this system makes it simpler to gather meteorological data. A number of industries, including as agriculture, transportation, emergency management, and event planning, are encouraged to make data-based decisions since users can quickly obtain current meteorological conditions and forecasts through user-friendly web interfaces or mobile applications

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture

Significance of Artificial Intelligence in the Production of Effective Output in Power Electronics

The power electronics (PE) industry is expected to play a significant role in the development of energy conservation and global industrialization trends of the 21st century. Due to the technological advancements that have occurred in the field, such as transportation and communication, the need for efficient and quality products is becoming more prevalent. The importance of power electronics is acknowledged in the automated industries that are constantly striving to improve their efficiency and effectiveness. Due to the increasing global energy consumption, the need for more energy-efficient technologies is also becoming more prevalent. Around 87% of our energy is derived from fossil fuels, while 6% is generated from nuclear power plants and 7% from renewable sources. Due to the increasing concerns about the environment and safety issues associated with nuclear plants and fossil fuels, the need for energy conservation is becoming more prevalent. This is also expected to be achieved through the development of power electronics. In the coming decades, the development of artificial intelligence (AI) tools, such as neural network, expert system, and fuzzy logic, is expected to bring a new era to the field of motion control and power electronics. Despite the technological advancements that have occurred in the field, these tools have not yet reached the power electronics sectors. In this paper, the AI tools and their applications in the field of power electronics and motion control are discussed

Assessment of Seismic Hazards in Underground Mine Operations using Machine Learning

The most common causes of coal mining accidents are seismic hazard, fires, explosions, and landslips. These accidents are usually caused by various factors such as mechanical and technical failures, as well as social and economic factors. An analysis of these accidents can help identify the exact causes of these accidents and prevent them from happening in the future. There are also various seismic events that can occur in underground mines. These include rock bumps and tremors. These have been reported in different countries such as Australia, China, France, Germany, India, Russia, and Poland. Through the use of advanced seismological and seismic monitoring systems, we can now better understand the rock mass processes that can cause a seismic hazard. Unfortunately, despite the advancements, the accuracy of these methods is still not perfect. One of the main factors that prevent the development of effective seismic hazard prediction techniques is the complexity of the seismic processes. In order to carry out effective seismic risk assessment in mines, it is important that the discrimination of seismicity in different regions is carried out. The widespread use of machine learning in analyzing seismic data, it provides reliability and feasibility for preventing major mishaps. This paper provides uses various machine learning classifiers to predict seismic hazards

Globalization and Public Health: An Examination of Cross-Border Health Issues

The rapid interconnection facilitated by globalization intensifies the dissemination of infectious diseases, posing substantial obstacles for public health systems globally. This paper utilizes a comparative methodology to analyze the impact of globalization on the dynamics of health issues that transcend national borders. It does so by closely examining two distinct pandemics: COVID-19 and the Nipah virus. Utilizing epidemiological data, public health policies, and scholarly literature, we examine the transmission patterns, susceptibilities, and strategies for addressing both viruses. By contrasting the easily transmissible and airborne characteristics of COVID-19 with the localized outbreaks and zoonotic source of the Nipah virus, we expose the varied difficulties presented by distinct cross-border health hazards. The main discovery we made emphasizes the contradictory connection between globalization and the readiness of public health. Interconnectedness not only speeds up the spread of viruses, but also promotes international collaboration in areas such as research, surveillance, and sharing of resources. We contend that effectively addressing cross-border health threats necessitates a nuanced comprehension of the dual nature of globalization, highlighting the importance of strong national health systems in conjunction with intensified global cooperation. This paper seeks to offer valuable insights to policymakers and public health professionals by analyzing the divergent cases of COVID-19 and Nipah virus. It aims to assist them in effectively managing the intricate relationship between globalization and health concerns that transcend national borders. We promote a proactive strategy that utilizes the advantages of international collaboration while enhancing local capacity to guarantee efficient readiness and reaction to forthcoming pandemics

Geographically Secured SSL-VPN Using GPS

We are moving towards an era where location information will be necessary for access control. The use of location information can be used for enhancing the security of an application, for critical applications, such as the military. A formal model for location-based access control is needed that increases the security of the application and ensures that the location information cannot be exploited to cause harm. In this paper, we show how the SSL VPN model can be extended to incorporate the notion of location. We show how this location information can be used to determine whether a subject has access to a given object. A novel improved VPN (Virtual Private Network) system based on geo-secured SSL (Secure Socket Layer) protocol is proposed to overwhelm the defects of traditional SSL VPN. It enhances current security applications granting access to sensible information and privileges to execute orders only to entities that are in a trusted location. This system not only authenticates authorized user but also the location of the userby Suresh Limkar and Dhiren Pat

Real Time Health Monitoring Using GPRS Technology

The concept of the project taken from Telemedicine. It is a rapidly developing application of clinical medicine where medical information is transferred through interactive audiovisual media for the purpose of consulting, and sometimes remote medical procedures or examinations. Telemedicine may be as simple as two health professionals discussing a case over the telephone, or as complex as using satellite technology and videoconferencing equipment to conduct a real-time consultation between medical specialists in two different countries. Telemedicine generally refers to the use of communications and information technologies for the delivery of clinical care. Care at a distance (also called in absentia care), is an old practice which was often conducted via post. There has been a long and successful history of in absentia health care which, thanks to modern communication technology, has evolved into what we know as modern telemedicine. In its early manifestations, African villagers used smoke signals to warn people to stay away from the village in case of serious disease. In the early 1900s, people living in remote areas in Australia used two-way radios, powered by a dynamo driven by a set of bicycle pedals, to communicate with the Royal Flying Doctor Service of AustraliaAdvances in sensor technology, personal mobile devices, and wireless broadband communications are enabling the development of an integrated personal mobile health monitoring system that can provide patients with a useful tool to assess their own health and manage their personal health information anytime and anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful integrated information management tools and play a major role in many people's lives. Here I focus on designing a Mobile health-monitoring system for people who stay alone at home or suffering from Heart Disease. This system presents a complete unified and mobile platform based connectivity solution for unobtrusive health monitoring. Developing a hardware which will sense heart rate and temperature of a patient, using Bluetooth modem all information lively transmitted to smart phone, from smart phone all information transmitted to server using GPRS. At server the received data compared with the standard threshold minimum and maximum value. The normal range of heart rate is 60 to 135 and the temperature of the patient is said to be normal above 95^F and below 104^F. If at all the rate increases above 145 or decreases below 55,it may be fatal and if it crossed this threshold limit then SMS will be sent to the relative of patient and Doctors along with measured values. The build-in GPS further provides the position information of the monitored person. The remote server not only collects physiological measurements but also tracks the position of the monitored person in real time. For transmitting data from Smartphone to the server using GPRS, here we need to create a website on data will be continuously transmitted from Smartphone to the website and from website data will be downloaded continuously on the server. Thus the system helps in tracking down the patient without getting the patient into any sort of communication. Undue mishaps can be avoided within the golden hours after a patient is struck with a heart attack