One of the main reasons for accidents among workers is harmful gas leakage. Many people die in chemical industries and their surrounding areas. The present invention is responsible for monitoring and controlling hazardous toxic gases like nitrogen dioxide (NO₂), carbon monoxide, ozone (O₃), sulfur dioxide (SO₂), LPG, hydrocarbon gases, silicones, hydrocarbons, alcohol, CH₄, hexane, benzine, as well as environmental conditions, such as temperature and relative humidity to prevent industrial accidents. The Arduino UNO R3 board is used as the central microcontroller. It is connected to the Cloud via AQ3 sensor, Minipid 2 HS PID sensor, IR5500 open path infrared gas detector, DHT11 Temperature and Humidity Sensor, MQ3 sensor, and ESP8266 and WIFI Module, which can store real-time sensor data and send alert messages to the industrys safety control board. Machine learning and artificial intelligence will be used to make an intelligent prediction (AI). The information gathered will be examined in real-time. The real-time data provided through the sensor can be accessed worldwide. Sensor data quality is critical in the Internet of Things (IoT) applications because poor data quality renders them useless. Error detection in sensor data improves the IoT-based toxic gas monitoring, controlling, and prediction system. Live data from sensors or datasets should be analyzed properly using appropriate techniques. Hence, hybrid hidden Markov and artificial intelligence models are applied as an error detection technique in the sensor dataset. This technique outperformed the dataset gas sensor array under dynamic gas mixtures and lived data. Our method outperformed harmful gas monitoring and error detection in sensor datasets compared to other existing technologies. The hybrid HMM and ANN fault detection methods performed well on the datasets and produced 0.01 false positive rate.

The convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid.

One of the most pressing issues in the current COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest possible cost and during the disease's early stages. Deep learning-based models have the potential to provide an accurate and efficient approach for the identification and diagnosis of COVID-19, with considerable increases in sensitivity, specificity, and accuracy when used in the processing of modalities. COVID-19 illness is difficult to detect and recognize since it is comparable to pneumonia. The main objective of this study is to distinguish between COVID-19-positive images and pneumonia-positive images. We have proposed an integrated convolutional neural network focused on discriminating against COVID-19-infected patients and pneumonia patients. Preprocessing is done on the image datasets. The novelty of this research work is to differentiate the COVID-19 images from the pneumonia images. It will help the medical experts in the decision-making. In order to train the model, the image is given directly as input to integrated convolutional neural network architecture; after training the model, the system is integrated with three different kinds of datasets: COVID-19 image dataset, RSNA pneumonia dataset, and a new dataset created from COVID-19 image dataset. The attainment of the system is evaluated by calculating the measures of sensitivity, specificity, precision, and accuracy, and this system produces the accuracy values of 94.04%, 97.2%, and 97.5% for the above datasets, respectively.

The objective of this study was to investigate the toxic effects of arsenic on testicular measurements and histology of adult Teddy goats bucks and to examine whether these toxic effects are scavenged by vitamin E. Twelve adult Teddy goat bucks were divided randomly into three groups, A, B and C. Group A was kept as control, the B was given sodium arsenite 5 mg/kg BW per day, and group C was fed with vitamin E 200 mg/kg BW per day + arsenic 5 mg/kg BW per day. This treatment was continued for 84 days. Analysis of data revealed that the testicular measurements (scrotal circumference, width, length and weight) were significantly reduced in arsenic‐treated animals, whereas there were ameliorating effects of vitamin E on these parameters. The major histopathological changes were present in the form of loss of germinal epithelium, atrophy of Leydig cells and vacuolations. Vitamin E in combination with sodium arsenite increased the active spermatogenesis as well as restoration of germinal epithelium. It can be concluded from the present findings that sodium arsenite causes toxicity in the male reproductive system of Teddy goat bucks with major changes in parenchyma of testes. Supplementation of vitamin E has protective effects on the toxicity of sodium arsenite on the reproductive system of male Teddy goat bucks.

Currently, IT computers are one of the most attractive technologies due to the ability to reduce computational costs by increasing the flexibility and scale of IT processes. In recent years, cloud computing has become a promising concept in one of the fastest parts of the IT industry. Such organizations have expressed concerns about critical issues regarding broad implementation of cloud computing (for example, security). This concern is since the data is stored remotely by the client; In fact, it can be saved everywhere. Security is one of the most accessible problems in cloud computing; Different companies watch cloud computing carefully for security risks. Private security and private threats can be less than cloud computing than those that must be stored in a single machine, instead of in the cloud (a network of computers used for external registration and conservation). To fully assess the profitability of cloud computing, it is necessary to compare the benefits of cloud computing and the risks to the current situation. Thus, customers have problems when they consider cloud computing for their business. In this article, we summarize the issues of reliability, availability and security of cloud computing (RAS issues), and provide feasible and accessible solutions.

Wireless sensor networks (WSN), a plurality of sensors in an energy limited deliveries. the sensors are arranged randomly for many applications. Thus, the battery Or download a replacement would be practical. Therefore, the energy efficient routing protocol to expand the network. In this article we propose a new cluster based Re-Leach dynamic Protocol Dynamic Reclustering based Leach protocol (DR-Leach), the expansion of the lattice energy consumption and reduce the age. The idea is that the energy cluster leaders next production cluster to balance In each round, the same number of nodes in the network of life. Make your first calculation It calculates the optimum amount of CHS in each round, and the optimum amount for each cluster. The results showed that the improvement of reliability protocols proposed frame and the total energy consumption than BCDCP Leach and protocols.