Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

Flash flooding is one of the most significant natural disasters in arid/hyperarid regions and causes vast property damage and a large number of deaths. For mitigating and reducing flood risks, data from several remote sensing satellite images—Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Landsat 8 Operational Land Imager (OLI), and Tropical Rainfall Measuring Mission (TRMM)—were prepared and combined through a GIS-based multicriteria decision-making technique to test and delineate the flash flood vulnerable areas of Wadi Hali in southwestern Saudi Arabia. Several flash flood thematic layers representing topographic, geomorphic, climatic, and hydrological conditions were prepared, normalized, and combined through a GIS- based analytic hierarchy process (AHP) technique to obtain flash flood hazard zones (FFHs). This method successfully presented a satisfactory output map that revealed six zones of flood risk, and areas of extreme hazard covered about 13% of the entire basin. Landsat 8 band composite 7, 5, and 3 and field data validated the FFHs. This map considered a key requirement for sustaining safe settlements downstream of Wadi Hali. Overall, the integration of remote sensing and GIS techniques revealed significant areas of flash flood zones in an arid region.

Phishing is a harmful online attack that could lead to identity theft and financial damages. The demand for high-accuracy phishing detection tools has risen due to the increase of online electronic services and payment systems. Most phishing detection techniques depend on features related to webpage content, which necessitates crawling the webpage and relying on third-party services. Relying on features related to webpage content could not provide high detection accuracy and leads to high false detection rates. Recently, deep learning has become a popular approach for detecting phishing websites. However, limited attention has been given to the generative adversarial network (GAN). This paper proposes a phishing detection model called PDGAN that depends only on a website’s uniform resource locator (URL) to achieve reliable performance. We use a long short-term memory network (LSTM) network as a generator of synthetic phishing URLs and a convolutional neural network (CNN) as a discriminator to decide whether the URLs are phishing or legitimate. We use a dataset containing nearly two million phishing and legitimate URLs obtained through PhishTank and DomCop. The experimental results show that the PDGAN achieves a detection accuracy of 97.58% and a precision of 98.02% without depending on third-party services and with greater accuracy than the state-of-the-art models.