Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents' sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a "fingerprint and timing-based snooping (FATS)" attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber-physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods.
Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA.
Pressure ulcer (PU) is a localized injury to the skin or underlying tissues usually over a bony prominence, which results due to pressure or pressure in combination with shear. It is an expensive health care problem that have deterring impact on the length of hospitalization and cause extra nursing care time. Moreover, PUs negatively impacts patients' health related quality of life. High PUs prevalence figures were found in specialized hospital units such as intensive care unit (ICU), orthopedics, surgery, and also in stroke patients in medical units. The major purpose of this study is to assess the frequency of pressure ulcers in stroke patients at Ayub teaching hospital. The methodology used for carrying out the research was cross-sectional study conducted during months of September, October, and November 2020. Questionnaire was used to collect the data and well-informed written consent was taken from the patients. A total of 120 stroke patients were initially included with the intention to study the frequency of PUs among them. Different age groups were taken but majority (48.3%) belonged to the age group 31-60 years. Maximum patients were hypertensive (65%), while few of them were diabetic (35%). From the results of proposed work, it is found that out of 120 stroke patients, 75.8% presented with ischemic stroke while 24.2% presented with hemorrhagic stroke. 8.3% that is 10 out of 120 stroke patients developed pressure ulcers of grade 1 (1.7%), grade 2 (1.7%), grade 3 (2.5%), and grade 4 (2.5%) mostly in the sacral region (6.7%) and also on ankle (0.8%), and shoulder (0.8%) respectively. Patients in the study group had unsatisfactory hygiene (6.7%) were malnourished (11.7%) and were not using preventive mattresses (79.2%). Those at the risk of developing pressure ulcers were not being repositioned (6.7%) and did not had awareness (10%). Prevention and treatment used in ward is 100%. Conclusively, the frequency of pressure ulcers in stroke patients was determined to be 8.3% and the most frequent localization was sacrum. The PU care in this hospital is appropriate but still could be improved further by improving risk assessment, prevention specially use of air mattress and patient education regarding PUs. The main objective of the study is to identify the frequency of PUs in stroke patients and to highlight various factors that would avoid PUs development.