Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

Federated Adversarial Learning (FAL) maintains the decentralization of adversarial training for data-driven innovations while allowing the collaborative training of a common model to protect privacy facilities. Before sharing with bigger global aggregation, it allows users to change settings locally over many iterations. However, a strong network against attackers in Industry 5.0 towards consumer digital ecosystems is a challenge for adversarial training methodologies. To solve this issue, a novel FAL-based Customized Inequality-Aware Federated Learning (CusIAFL) technique is proposed in this paper for classifying and securing color images. The proposed method reduces the instability brought on by the heterogeneity of the data and optimizes each data sample by understanding the client-label distribution. A unique Pix2Pix Generative Adversarial Network (GAN) algorithm is employed to generate realistic images in the presented research work, while a hybrid approach is used to guarantee consistency in the time series data. This innovative research work is evaluated on various non-medical, consumer electronic, and medical imagery. The experimental results have been evaluated using performance metrics, namely accuracy, entropy, Peak Signal-to-Noise Ratio (PSNR), Hausdorff Distance (HD95), Structural Similarity Index (SSIM), and Mean Square Error (MSE). The results show that the proposed technique outperforms the existing models in terms of security.

Power Quality Analyzers (PQA) play an important role in monitoring and controlling health of the electrical systems. They can report the fluctuations in the field measurements with different power quality issues as well as due to load variations. Internet of Things (IoT) is a potential technology to design Smart PQAs for remote monitoring and easy integration of the field information on the cloud platform using gateway units. This paper focuses on the development of a Smart PQA system using low-cost IoT hardware and software design solutions. The hardware development is completed using Arduino Mega 2560 microcontroller in combination with ESP32 Wroom Wi-Fi gateway and SIM900A GSM gateway. The real-time field data is gathered at the ThingSpeak platform for future analysis, while GSM-based design ensures timely alerts to the end users for any major fluctuation in the power supply. The performance of the proposed low-cost system has been compared with the readings obtained from FPGA-based conventional PQA and standard Fluke Meter when connected to different loads. The proposed system output values are tabulated and compared using graphs. The proposed system is expected to be useful for the critical assessment, monitoring, and control of power quality parameters in various commercial and residential premises.

Machine Learning (ML) is a dynamic method for managing extensive datasets to uncover significant patterns and hidden insights. ML has revolutionized numerous industries, from healthcare to finance, and from entertainment to transportation. Ensemble classifiers combined with Explainable AI (XAI) have surfaced as a significant asset in the field of Alzheimer's Disease (AD) diagnosis. Boosting EC techniques coupled with Shapley Additive Explanations (SHAP) offers a powerful approach to AD diagnosis. This paper investigates boosting ensemble ML schemes, such as XGBoost, LightGBM, and Gradient Boosting (GB), for AD diagnosis and SHAP for feature selection. The proposed scheme achieved efficient results, with an accuracy of more than 94% with minimum features for the detection process.

Disturbances in the electrical network in residential, commercial, and industrial settings often lead to equipment malfunction, shorter component lifespan, and higher operational costs. Therefore, it is essential to monitor irregularities in power supply to ensure improved performance. Power Quality Analysers (PQA) play an essential role in managing the health of electrical systems by measuring all fluctuations in real-time. This study focuses on the experimental analysis conducted using an Internet of Things based smart PQA system. The authors designed a low-cost hardware system using ZMPT101B and ACS712 voltage and current sensors connected with an Arduino Mega microcontroller and ESP32 Wroom gateway. The proposed system monitors several important parameters such as voltage, current, power, frequency, power factor, and line-to-line voltages from three-phase systems and stores all data online using the ThingSpeak platform. The main contribution of this experimental case study is to create a real-time power quality data collection system using IoT sensor network and comparing its performance against conventional FPGA-based PQA systems. The proposed Smart PQA system promises a cost effective solution to collect real-time power quality data on the cloud platform for timely decision making. However, the error analysis using mean squared error, root mean squared error and %error against the conventional system also highlights the possible applications & future scope in the improvement of the proposed design. In future, this system can be scaled to achieve wide range of applications such as timely monitoring, critical assessment, and control of power quality parameters in residential, commercial, and industrial settings.