Due to energy-intensive processes, dependence on fossil fuels and poor waste management, industry is among the largest contributors to global carbon emissions This environment this growing environmental problem highlights the urgent need for sustainable solutions to reduce carbon emissions. This study examines the role of green technologies in addressing these challenges, focusing on renewable energy systems, carbon capture and storage (CCS), circular economic practices, and digital products on the other hand. The main objective is to assess how these technologies can reduce greenhouse gas emissions and increase energy efficiency and potentially increase the industrial sustainability of resources activities in the field. Findings show that integrating green technologies can lead to a 25% reduction in energy consumption, 85% efficiency in carbon capture, and greenhouse gas emissions has decreased by 50% in a decade Furthermore, sustainable materials and energy efficiency solutions promise to reduce waste and improve the relative quality of infrastructure use Display results. Despite constraints such as high start-up costs and technical barriers, the study highlights the significant potential of green innovation to transform technology It provides a call to action for businesses , policy makers and consumers to embrace this technology as a means to achieve global sustainability goals ends.

The rapidly growing Internet of Things (IoT) devices pose significant security challenges due to their decentralized nature, limited computing power and reliance on centralized security models Traditional IoT security systems often face issues such as single point of failure, limited scalability and vulnerability to distributed denial of service (DDoS) attacks They also require la-doable security solutions. Blockchain technology has emerged as a potential solution due to its decentralized, immutable and transparent nature, providing advanced security for IoT environments. This study proposes a blockchain-based security strategy aimed at mitigating security risks in IoT networks. The system uses smart contracts to perform basic functions such as device authentication, data integrity verification, and access control. The main goal of the research is to develop IoT security solutions that increase device reliability speeds, reduce power consumption, reduce connection latency, and maximize network availability. The results of the survey show significant improvements over existing IoT security measures. The blockchain-based system achieves device authentication time of 0.1 to 2 seconds, 95% detection rate for unauthorized access, and supports 1000 devices, double the capacity of traditional models Also, the system reduces energy consumption 0.5–2 joules per transaction and transaction latency to 1–5 seconds , making it suitable for real-time IoT applications.

Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients’ fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.

With regard to the forthcoming requirements for mobile services in 5G networks, several new technologies have recently become the focus of leading-edge research. One of those is in-band full-duplex communications in processing data with bigdata based Apache spark. The idea is to simply employ the same frequency band to simultaneously transmit and receive information, allowing more spectrally efficient communications when compared to the traditional half-duplex or out-of-band full-duplex counterparts. By breaking a long-held assumption in wireless communications, in-band full-duplex. this paper aims to study wireless communication in 5G network with use of bigdata based Apache spark. Particularly, apache spark suppression filters are studied, as well as feedback adaptive filtering is proposed for relay systems with multiple-input multiple-out (MIMO) antennas. In this case, the relay system energy efficiency is maximized by finding the optimal transmit powers, while maintaining a certain individual link quality. For this scenario, the effect of massive MIMO is likewise addressed, and an algorithm that maximizes the system total achievable rate is derived. 5G technologies have pressing security challenges to secure data integrity and privacy in critical wireless communications requiring extensive research before implementing these technologies into use. This paper examines the new possibilities 5G networks offer for wireless network communication with high bandwidth of 90 GHz. The prediction of 5G network bandwidth maximum recoded at 90 GHz on 5 nodes of apache spark at 80% of data trained with total 12 nodes. The main question is will the 5G technology offers sufficient performance for wireless communication.

Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things.