Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.

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Machine learning and parallel processing are extremely commonly used to enhance computing power to induce knowledge from an outsized volume of data. To deal with the problem of complexity and high dimension, machine learning algorithms like Deep Reinforcement Learning (DRL) are used, while parallel processing algorithms like Parallel Particle Swarm Optimization (PPSO) are parallelized to speed up the operation and reduce the processing time to train the neural network. Due to the arrival of a large number of incoming tasks in the cloud environment, load balancing is an important issue. To solve this problem, the datacenter controller or an agent makes an intelligent decision to handle a large number of tasks within a minimum time period or at high speed. In this work, we proposed an effective scheduling algorithm named Deep Reinforcement Learning with Parallel Particle Swarm Optimization (DRLPPSO) to solve the load balancing problem and its various parameters with greater accuracy and high speed. Our experimental results show that our proposed scheduling algorithm increases the reward by 15.7%, 12%, and 13.1% when the task set is 2000 and improves the reward by 17.5%, 12.6%, and 15.3% when the task set is 4000, as compared to the Modified Particle Swarm Optimization (MPSO), Asynchronous Advantage Actor-Critic (A3C), and Deep Q-Network (DQN) techniques.

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.

The novel paradigm of Internet of Things (IoT) is gaining recognition in the numerous scenarios promoting the pervasive presence of smart things around us through its application in various areas of society, which includes transportation, healthcare, industries, and agriculture. One more such application is in the smart office to monitor the health of devices via machine learning (ML) that makes the equipment more efficient by allowing real-time monitoring of their health. It guarantees indoor comfort as per the user’s satisfaction as it emphasizes on fault prediction in real-life devices. Early identification of various types of faults in IoT devices is the key requirement in smart offices. IoT devices are becoming ubiquitous and provide an assistant to supervise an office that is regulated by ML and data received from sensors is stored in cloud. A recommender system facilitates the selection of an appropriate solution for faults in IoT-enabled devices to mitigate faults. The architecture proposed in this paper is used to monitor each and every office appliance connected via IoT technology using ML technique, and recommender system is used to recommend solutions for fault patterns without much human intervention. The ultrasonic motion sensor is used to fetch the information of employee availability in cubicles and data is sent to the cloud through the WiFi module. ATmega8 is used to control electrical appliances in the office environment. The significance of this work is to forecast the faults in IoT appliances which will have an impact on life and reliability of IoT appliances. The main objective is to design a prototype of a smart office using IoT that can control and automate workplace devices and forecast whether the device needs repairing or replacing, thus reducing the overall burden on the employee and helping out in increasing physical as well as mental health of the person.