Statistical and machine learning techniques are frequently employed in the study of neuroimaging data for finding Alzheimer disease (AD) in clinical studies and in additional inquiries about research settings. AD affects the whole brain and as a result the quality of life, where most affected regions are the hippocampus (HP), middle temporal gyrus (MTG), entorhinal cortex, and posterior cingulate cortex (PCC). We used well‐known classification methods to diagnose the affected regions of the brain at different stages of age using biomarker modalities and functional magnetic resonance imaging (fMRI) at the resting state, and later marked the affected brain region on MRI. We have used well‐known support vector machine (SVM), Fisher's linear discriminant analysis, artificial neural network, and logistic regression for the classification of AD. In the context of receiver operating characteristic (ROC) curves, an SVM provided the best classification among AD stages. Moreover, analysis showed development of AD.

The current paper aims to highlight: 1) Major problems due to urbanization, including land cutting, erosion, overgrazing, biodiversity loss, and climate change. 2) The impact of grazing on plant community structure and ecosystem functioning. 3) Management and conservation of natural ecosystems in Sheikh Muhammdi Peshawar. For the current work, three different sites (Zones 1, 11, 111) were selected in the local area. The populations of the local area have increased very rapidly in the last 40 to 50 years. Anthropogenic activities associated with population and industrialization in the district, with vegetation clear for developing of towns and roads, has resulted in the substitution of vegetations with the dark color surface, the temperature of the environments much higher than before, leading to the phenomenon of the urban heat island effect. This urbanization and construction work at Amman plots Sheikh Muhammadi Peshawar is causing the extinction of vegetation and there would be no more wild vegetation in the near future in that particular area.

Objective: To determine the frequency of different hair loss using BASP classification in Pakistani men. Study Design: Cross-Sectional Study. Setting: Study was conducted at Department of Dermatology, Abbasi Shaheed Hospital, Karachi. Duration: Six months starting 6th August 2019 till 5th January 2020 Material and Methods: Total 157 diagnosed patients with hair loss who met the diagnostic criteria were included. Brief history was taken and demographic information was recorded after taking written informed consent. Male pattern of hair loss (MPHL) was checked and categorized using BASP classification. Data was analyzed by SPSS 24.0. Results: In this study out of 157 patients, mean and standard deviation of age and duration of hair loss were 33.14±12.49 years and 1.89± 0.44 years, respectively. The Pattern of hair loss distribution showed that 34 (21.7%) were L type, 66 (42%) were M type, 35 (22.3%) were C type, and 22 (14%) were U type patterned hair loss. Conclusion: Assessment of male pattern hair loss using BASP classification found that M type hair loss was more prevalent. Currently, there are effective medical and surgical treatments available for men. However, the knowledge of pattern of hair loss in our population would help in choosing suitable treatment plans. Keywords: Male Pattern hair loss, Androgenic alopecia and BASP classification