Exploring Current Trends and Challenges in Cybersecurity: A Comprehensive Survey

Cyber security is the process of preventing unauthorized access, theft, damage, and interruption to computers, servers, networks, and data. It entails putting policies into place to guarantee the availability, confidentiality, and integrity of information and information systems. Cyber security seeks to protect against a variety of dangers, including as hacking, data breaches, malware infections, and other nefarious actions.  Cyber security has grown to be a major worry as a result of the quick development of digital technology and the growing interconnection of our contemporary society. In order to gain insight into the constantly changing world of digital threats and the countermeasures put in place to address them, this survey seeks to study current trends and issues in the area of cyber security. The study includes responses from end users, business executives, IT administrators, and experts across a wide variety of businesses and sectors. The survey gives insight on important problems such the sorts of cyber threats encountered, the efficacy of current security solutions, future technology influencing cyber security, and the human elements leading to vulnerabilities via a thorough analysis of the replies. The most important conclusions include an evaluation of the most common cyber dangers, such as malware, phishing scams, ransom ware, and data breaches, as well as an investigation of the methods and tools used to counter these threats. The survey explores the significance of staff education and awareness in bolstering cyber security defenses and pinpoints opportunities for development in this area. The survey also sheds insight on how cutting-edge technologies like cloud computing, artificial intelligence, and the Internet of Things (IoT) are affecting cyber security practices. It analyses the advantages and disadvantages of using these technologies while taking into account issues like data privacy, infrastructure security, and the need for specialized skills. The survey also looks at the compliance environment, assessing how industry norms and regulatory frameworks affect cyber security procedures. The survey studies the obstacles organizations encounter in attaining compliance and assesses the degree of knowledge and commitment to these requirements. The results of this cyber security survey help to better understand the current status of cyber security and provide organizations and individual’s useful information for creating effective policies to protect digital assets. This study seeks to promote a proactive approach to cyber security, allowing stakeholders to stay ahead of threats and build a safe digital environment by identifying relevant trends and concerns

A Novel Hybrid Based Method in Covid 19 Health System for Data Extraction with Blockchain Technology

Millions of people have been afflicted by the COVID-19 epidemic, which has resulted in hundreds of thousands of fatalities throughout the world. Extracting correct data on patients and facilities with and without COVID-19 with high confidence for medical specialists or the government is extremely difficult. As a result, utilizing blockchain technology, a reliable data extraction methodology for the COVID-19 database is constructed. In this accurate data extraction model development and validation study in blockchain technology for COVID analysis, here a novel Hybrid Deep Belief Lionized Optimization (HDBLO) approach is proposed. The weights of the deep model are optimized by the fitness of lion optimization. The implementation of this work is executed using MATLAB software. The simulation outcomes shows the effective performance of proposed model in blockchain technology in COVID paradigm in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), accuracy, F-measure, Processing time, precision and error. Consequently, the proposed approach is compared with the conventional strategies for significant validation

A Comprehensive Survey of Deep Learning: Advancements, Applications, and Challenges

Artificial intelligence's "deep learning" discipline has taken off, revolutionizing a variety of industries, from computer vision and natural language processing to healthcare and finance. Deep learning has shown extraordinary effectiveness in resolving complicated issues, and it has a wide range of potential applications, from autonomous vehicles to healthcare. The purpose of the survey to study deep learning's present condition, including recent advancements, difficulties, and constraints since the subject is currently fast growing. The basic ideas of deep learning, such as neural networks, activation functions, and optimization algorithms, are first introduced. We next explore numerous topologies, emphasizing their distinct properties and uses, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Further concepts, applications, and difficulties of deep learning are all covered in this survey paper's thorough review. This survey aid the academics, professionals, and individuals who want to learn more about deep learning and explore its applications to challenging situations in the real world

Experimental Performance Analysis Of Free And Forced Fully Developed Air Flow Green House Solar Dryer Using Curry Leaves

The world is beginning to move away from its consumption of fossil fuels. Various technologies are being developed to make use of renewable energy sources such as wind, solar, and tidal, etc. Solar energy is the best choice among these sources because of it is readily available, abundant, and capable of producing both electric energy and space heating. Solar energy can be used directly or indirectly to dry agricultural and non-agricultural products to preserve them for long a period without formation of fungi. Drying of herbal leaves is an important process in Siddha and Ayurvedic industries to produce herbal medicines in power form. However, as herbal leaves are dried in the open sun, they are susceptible to environmental factors such as rain, insects, and livestock. These disadvantages of open-air drying shall be overwhelmed by greenhouse solar dryer. Greenhouse solar dryer with natural convection, forced convection with hot air supply are the existing methods, but when supplied with hot air, the rise in temperature leads to nutrient loss in herbal leaves. In order to avoid this loss in nutrients, the current work gives a solution that the temperature of forced convection greenhouse dryer can be reduced and controlled by supplying the ambient air at inlet flow in a fully developed air region, and this method can also leads to reduction in colour loss with possibly same or higher drying rate compare to natural convection greenhouse dryer

Performance Comparison of Tray, Bed and Integrated Drying Chamber in Closed Loop Heat Pump Dryer for Bermuda Grass

Drying plays a crucial role in various industries such as food production, agriculture, Siddha, Ayurveda, and medical fields. To achieve controlled drying conditions, a heat pump dryer is considered an effective method, allowing for precise control of parameters like temperature, humidity, and air velocity. In this study, a heat pump dryer was designed and constructed to investigate the drying characteristics of Bermuda grass (Cynodon dactylon) at different velocities (1.5 m/s, 2.0 m/s, and 2.5 m/s) using three types of drying chambers: fluidized bed dryer, tray dryer, and combined dryer (a combination of bed and tray). The heat pump system utilized R134a as the refrigerant. The performance of the heat pump dryer in the three drying chambers was analyzed using Bermuda grass as the drying product. The Moisture Removal Rate (MRR) was calculated for various combinations of velocity and drying chamber, and it was observed that the combined dryer achieved a higher MRR at all three velocities compared to the tray and fluidized bed dryers

Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment

Debris flows triggered by rainfall are catastrophic geohazards that occur compounded during extreme events. Few early warning systems for shallow landslides and debris flows at the territorial scale use thresholds of rainfall intensity–duration (ID). ID thresholds are mostly defined using hourly rainfall. Due to instrumental and operational challenges, current early warning systems have difficulty forecasting sub-daily time series of weather for landslides in the Himalayas. Here, we present a framework that employs a spatio-temporal numerical model preceded by the Weather Research And Forecast (WRF) Model for analysing debris flows induced by rainfall. The WRF model runs at 1.8 km × 1.8 km resolution to produce hourly rainfall. The hourly rainfall is then used as an input boundary condition in the spatio-temporal numerical model for debris flows. The debris flow model is an updated version of Van Asch et al. (2014) in which sensitivity to volumetric water content, moisture-content-dependent hydraulic conductivity, and seepage routines are introduced within the governing equations. The spatio-temporal numerical model of debris flows is first calibrated for the mass movements in the Kedarnath catchment that occurred during the 2013 North India floods. Various precipitation intensities based on the glossary of the India Meteorological Department (IMD) are set, and parametric numerical simulations are run identifying ID thresholds of debris flows. Our findings suggest that the WRF model combined with the debris flow numerical model shall be used to establish ID thresholds in territorial landslide early warning systems (Te-LEWSs).</p