Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model’s parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

Deep Neural Networks (DNN) have achieved state-of-the-art performance in medical image classification and are increasingly being used for disease diagnosis. However, these models are quite complex and that necessitates the need to reduce the model complexity for their use in low-power edge applications that are becoming common. The model complexity reduction techniques in most cases comprise of time-consuming operations and are often associated with a loss of model performance in proportion to the model size reduction. In this paper, we propose a simplified model complexity reduction technique based on reducing the number of channels for any DNN and demonstrate the complexity reduction approaches for the ResNet-50 model integration in low-power devices. The model performance of the proposed models was evaluated for multiclass classification of CXR images, as normal, pneumonia, and COVID-19 classes. We demonstrate successive size reductions down to 75%, 87%, and 93% reduction with an acceptable classification performance reduction of 0.5%, 0.5%, and 0.8% respectively. We also provide the results for the model generalization, and visualization with Grad-CAM at an acceptable performance and interpretable level. In addition, a theoretical VLSI architecture for the best performing architecture has been presented.

The perovskite-based solar cells (PSCs) are gaining much attention for application in solar cell device frameworks due to high absorption property, easy and low-cost fabrication, and tunable bandgap. The PSCs exhibiting conversion efficiency up to ∼22% are reported utilizing expensive and unstable electrons and hole transportation layers (ETL and HTL). However the stability of these devices drastically suffers under humid conditions and in an environment that is rich with ultraviolet radiation. The deterioration under such conditions produces Pb ions which are harmful to the biotic environment limiting its usefulness for practical device implantation. In this work, we propose the designing of methyl ammonium lead halide (CH₃NH₃PBI₃) based planar perovskite solar cell. The general-purpose solar cell simulation tool (GPVDM) is used to simulate and study the proposed design in detail. The format of the cell consists of indium tin oxide (ITO)/zinc oxide (ZnO)/CH₃NH₃PBI₃/Cu doped (2%) nickel oxide (Cu: NiOx)/Aluminum (AL). The HTL layer utilized in our study demonstrated a high stability (48%) in ultraviolet radiation. We also investigated the effect of active layer thickness, ETL and HTL layer, parasitic resistance, light intensity and operating temperature on proposed PSCs. The optimum layer thickness of active, ETL and HTL was found to be 400 nm and 150 nm respectively, while keeping the electrode thickness to 100 nm. At the optimum thickness, the device demonstrates fill factor (FF) and efficiency as 15.33% and 0.8516, respectively. The optimum device operating temperature was 285 k. The observed maximum FF and maximum efficiency reached up to 15.85% and 0.8574 respectively with thicker active, ETL/HTL layers. We observed that our HTL layer (Cu doped nickel oxide) shows stability of 66% against ultraviolet A and 48% against both ultraviolet A and B. This study provides a comprehensive numerical analysis for designing an efficient perovskite based solar cell which can be adopted for practical device fabrication.

Industrial control systems were designed as closed systems without any consideration or need for Internet connectivity. This design choice resulted in most SCADA protocol implementations being plain text with little security against attackers. Information on SCADA systems and protocols is readily available in public domain, making them a target for malicious attacks. Network coding is a lightweight technique based on simple XOR operations for error correction that can also help secure the message. This paper proposes use of network coding for protection of message interchanges with the field devices in an industrial network. The results show that the proposed method effectively protects the communications against a malicious adversary.